Udemy - 2019 AWS SageMaker and Machine Learning - With Python

Category: Technical

Tag: Perl/PHP/Python


Posted on 2019-05-25, by phaelx.

Description



Date: May 2019
Author: Chandra Lingam

Size: 4.6 GB
Format: MP4
Download     >>    https://dropapk.com/291zh0zwbz2o
What you'll learn
   *Learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization
   *Integrate predictive models with your application using simple and secure APIs
   *Convert your ideas into highly scalable products in days


Course content

Introduction and Housekeeping
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview

2019 SageMaker Housekeeping
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup

2019 Machine Learning Concepts
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More

2019 SageMaker Service Overview
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference

XGBoost - Gradient Boosted Trees
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost

SageMaker - Principal Component Analysis (PCA)
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary

SageMaker - Factorization Machines
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User

SageMaker - DeepAR Time Series Forecasting
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary

2019 Integration Options - Model Endpoint
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint

2019 SageMaker HyperParameter Tuning
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System

AWS Machine Learning Service
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz

Linear Regression
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz

AWS - Linear Regression Models
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz

Adding Features To Improve Model
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary

Normalization
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz

Adding Complex Features
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary

Kaggle Bike Hourly Rental Prediction
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary

Logistic Regression
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz

Onset of Diabetes Prediction
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz

Multiclass Classifiers using Multinomial Logistic Regression
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary

Text Based Classification with AWS Twitter Dataset
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary
   AWS Twitter Feed Classification for Customer Service
   Lab: Train, Evaluate Model and Assess Predictive Quality
   Lab: Interactive Prediction with AWS
   Logistic Regression Summary

Data Transformation using Recipes
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary
   AWS Twitter Feed Classification for Customer Service
   Lab: Train, Evaluate Model and Assess Predictive Quality
   Lab: Interactive Prediction with AWS
   Logistic Regression Summary
   Recipe Overview
   Recipe Example
   Text Transformation
   Numeric Transformation - Quantile Binning
   Numeric Transformation - Normalization
   Cartesian Product Transformation - Categorical and Text
   Summary

Hyper Parameters, Model Optimization and Lifecycle
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary
   AWS Twitter Feed Classification for Customer Service
   Lab: Train, Evaluate Model and Assess Predictive Quality
   Lab: Interactive Prediction with AWS
   Logistic Regression Summary
   Recipe Overview
   Recipe Example
   Text Transformation
   Numeric Transformation - Quantile Binning
   Numeric Transformation - Normalization
   Cartesian Product Transformation - Categorical and Text
   Summary
   Introduction
   Data Rearrangement, Maximum Model Size, Passes, Shuffle Type
   Regularization, Learning Rate
   Regularization Effect
   Improving Model Quality
   Model Maintenance
   AWS Machine Learning System Limits
   AWS Machine Learning Pricing

Integration of AWS Machine Learning With Your Application
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary
   AWS Twitter Feed Classification for Customer Service
   Lab: Train, Evaluate Model and Assess Predictive Quality
   Lab: Interactive Prediction with AWS
   Logistic Regression Summary
   Recipe Overview
   Recipe Example
   Text Transformation
   Numeric Transformation - Quantile Binning
   Numeric Transformation - Normalization
   Cartesian Product Transformation - Categorical and Text
   Summary
   Introduction
   Data Rearrangement, Maximum Model Size, Passes, Shuffle Type
   Regularization, Learning Rate
   Regularization Effect
   Improving Model Quality
   Model Maintenance
   AWS Machine Learning System Limits
   AWS Machine Learning Pricing
   Introduction
   Integration Scenarios
   Security using IAM
   Hands-on lab - List of Demos and Objective
   Lab: Enable Real Time End Point and Configure IAM Prediction User
   Lab: Invoking Prediction From AWS Command Line Interface
   Lab: Invoking Prediction From Python Client
   Lab: Python Client to Train, Evaluate Models and Integrate with AWS
   Lab: Invoking Prediction From Web Page AngularJS Client
   Demo Allowing Prediction Only For Registered Users
   Cognito Overview
   Lab: Cognito User Pool Configuration
   Lab: AngularJS Web Client - Invoke Prediction for authorized users
   Lab: Invoke Machine Learning Service From AWS EC2 Instance
   Summary

Homework - Additional Problems
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary
   AWS Twitter Feed Classification for Customer Service
   Lab: Train, Evaluate Model and Assess Predictive Quality
   Lab: Interactive Prediction with AWS
   Logistic Regression Summary
   Recipe Overview
   Recipe Example
   Text Transformation
   Numeric Transformation - Quantile Binning
   Numeric Transformation - Normalization
   Cartesian Product Transformation - Categorical and Text
   Summary
   Introduction
   Data Rearrangement, Maximum Model Size, Passes, Shuffle Type
   Regularization, Learning Rate
   Regularization Effect
   Improving Model Quality
   Model Maintenance
   AWS Machine Learning System Limits
   AWS Machine Learning Pricing
   Introduction
   Integration Scenarios
   Security using IAM
   Hands-on lab - List of Demos and Objective
   Lab: Enable Real Time End Point and Configure IAM Prediction User
   Lab: Invoking Prediction From AWS Command Line Interface
   Lab: Invoking Prediction From Python Client
   Lab: Python Client to Train, Evaluate Models and Integrate with AWS
   Lab: Invoking Prediction From Web Page AngularJS Client
   Demo Allowing Prediction Only For Registered Users
   Cognito Overview
   Lab: Cognito User Pool Configuration
   Lab: AngularJS Web Client - Invoke Prediction for authorized users
   Lab: Invoke Machine Learning Service From AWS EC2 Instance
   Summary
   Mushroom Classification

Conclusion
   Introduction
   Root Account Setup and Billing Dashboard Overview
   Enable Access to Billing Data for IAM Users
   Create Users Required For the Course
   AWS Command Line Interface Tool Setup and Summary
   Six Advantages of Cloud Computing
   AWS Global Infrastructure Overview
   Downloadable Resources
   Demo - S3 Bucket Setup
   Demo - Setup SageMaker Notebook Instance
   2019 Demo - Source Code and Data Setup
   2019 Introduction to Machine Learning, Concepts, Terminologies
   2019 Data Types - How to handle mixed data types
   2019 Introduction to Python Notebook Environment
   2019 Introduction to working with Missing Data
   2019 Data Visualization - Linear, Log, Quadratic and More
   Downloadable Resources
   SageMaker Overview
   Compute Instance Families and Pricing
   Algorithms and Data Formats Supported For Training and Inference
   Introduction to XGBoost
   Source Code Overview
   Demo - Create Files in SageMaker Data Formats and Save Files To S3
   Demo - Working with XGBoost - Linear Regression Straight Line Fit
   Demo - XGBoost Example with Quadratic Fit
   Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
   Demo - Kaggle Bike Rental Model Version 1
   Demo - Kaggle Bike Rental Model Version 2
   Demo - Kaggle Bike Rental Model Version 3
   Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
   Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
   Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
   How to remove SageMaker endpoints and Shutdown Notebook Instance
   Creating EndPoint From Existing Model Artifacts
   XGBoost Hyper Parameter Tuning
   Demo - XGBoost Multi-Class Classification Iris Data
   Demo - XGBoost Binary Classifier For Diabetes Prediction
   Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
   Summary - XGBoost
   Downloadable Resources
   Introduction to Principal Component Analysis (PCA)
   PCA Demo Overview
   Demo - PCA with Random Dataset
   Demo - PCA with Correlated Dataset
   Cleanup Resources on SageMaker
   Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
   Demo - PCA Local Model with Kaggle Bike Train
   Demo - PCA training with SageMaker
   Demo - PCA Projection with SageMaker
   Exercise : Kaggle Bike Train and PCA
   Summary
   Downloadable Resources
   Introduction to Factorization Machines
   MovieLens Dataset
   Demo - Movie Recommender Data Preparation
   Demo - Movie Recommender Model Training
   Demo - Movie Predictions By User
   Downloadable Resources
   Introduction to DeepAR Time Series Forecasting
   DeepAR Training and Inference Formats
   Working with Time Series Data, Handling Missing Values
   Demo - Bike Rental as Time Series Forecasting Problem
   Demo - Bike Rental Model Training
   Demo - Bike Rental Prediction
   Demo - DeepAR Categories
   Demo - DeepAR Dynamic Features Data Preparation
   Demo - DeepAR Dynamic Features Training and Prediction
   Summary
   Downloadable Resources
   Integration Overview
   Install Python and Boto3 - Local Machine
   Install SageMaker SDK, GIT Client, Source Code, Security Permissions
   Client to Endpoint using SageMaker SDK
   Client to Endpoint using Boto3 SDK
   Microservice - Lambda to Endpoint - Payload
   Microservice - Lambda to Endpoint
   Microservice - API Gateway, Lambda to Endpoint
   Downloadable Resources
   Introduction to Hyperparameter Tuning
   Lab: Tuning Movie Rating Factorization Machine Recommender System
   Lab: Step 2 Tuning Movie Rating Recommender System
   2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
   Python Development Environment and Boto3 Setup
   Project Source Code and Data Setup
   Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
   Lab: AWS S3 Bucket Setup and Configure Security
   Summary
   Introduction and House Keeping Quiz
   Optional: Machine Learning Where To Start (Article)
   Machine Learning Terminology
   Data Types supported by AWS Machine Learning
   Linear Regression Introduction
   Binary Classification Introduction
   Multiclass Classification Introduction
   Data Visualization - Linear, Log, Quadratic and More
   Algorithm and Terminology Quiz
   Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
   Lab: Linear Regression for complex shapes
   Summary
   Linear Regression Quiz
   Lab: Simple Training Data
   Lab: Datasource
   Lab: Train Model with default recipe
   AWS Models Quiz
   Concept - How to evaluate regression model accuracy?
   Lab: Evaluate predictive quality of the trained model
   Lab: Review Default Recipe Settings Used To Train model
   Lab: Train Model With Custom Recipe and Review Performance
   Model Performance Summary and Conclusion
   AWS Regression Metrics Quiz
   Lab: Quadratic Fit Training Data
   Lab: Underfitting With Linear Features
   Lab: Normal Fit With Quadratic Features
   Summary
   Lab: Impact of Features With Different Magnitude
   Concept: Normalization to smoothen magnitude differences
   Lab: Train Model With Feature Normalizaton
   Summary
   Underfitting and Normalization Quiz
   Lab: Prepare Training Data
   Lab: Adding Complex Features
   Lab: Train Model With Higher Order Features
   Lab: Performance Of Model With Degree 1 Features
   Lab: Performance of Model with Degree 4 Features
   Lab: Performance of Model With Degree 15 Features
   Summary
   Review Kaggle Bike Train Problem And Dataset
   Lab: Train Model To Predict Hourly Rental
   Lab: Evaluate Prediction Quality
   Linear Regression Wrapup and Summary
   Binary Classification - Logistic Regression, Loss Function, Optimization
   Lab: Binary Classification Approach
   True Positive, True Negative, False Positive and False Negative
   Lab: Logistic Optimization Objectives
   Lab: Logistic Cost Function
   Lab: Cost Example
   Optimizing Weights
   Summary
   Logistic Regression Quiz
   Problem Objective, Input Data and Strategy
   Lab: Prepare For Training
   Lab: Training a Classification Model
   Concept: Classification Metrics
   Concept: Classification Insights with AWS Histograms
   Concept: AUC Metric
   Lab: Review Diabetes Model Performance
   Lab: Cutoff Threshold Interactive Testing
   Lab: Evaluating Prediction Quality With Additional Dataset
   Lab: Batch Prediction and Compute Metrics
   Summary
   Logistic Regression Metrics Quiz
   Lab: Iris Classifcation
   Lab: Train Classifier with Default and Custom Recipe
   Concept: Evaluating Predictive Quality of Multiclass Classifiers
   Concept: Confusion Matrix To Evaluating Predictive Quality
   Lab: Evaluate Performance of Iris Classifiers using Default Recipe
   Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
   Lab: Batch Prediction and Computing Metrics using Python Code
   Summary
   AWS Twitter Feed Classification for Customer Service
   Lab: Train, Evaluate Model and Assess Predictive Quality
   Lab: Interactive Prediction with AWS
   Logistic Regression Summary
   Recipe Overview
   Recipe Example
   Text Transformation
   Numeric Transformation - Quantile Binning
   Numeric Transformation - Normalization
   Cartesian Product Transformation - Categorical and Text
   Summary
   Introduction
   Data Rearrangement, Maximum Model Size, Passes, Shuffle Type
   Regularization, Learning Rate
   Regularization Effect
   Improving Model Quality
   Model Maintenance
   AWS Machine Learning System Limits
   AWS Machine Learning Pricing
   Introduction
   Integration Scenarios
   Security using IAM
   Hands-on lab - List of Demos and Objective
   Lab: Enable Real Time End Point and Configure IAM Prediction User
   Lab: Invoking Prediction From AWS Command Line Interface
   Lab: Invoking Prediction From Python Client
   Lab: Python Client to Train, Evaluate Models and Integrate with AWS
   Lab: Invoking Prediction From Web Page AngularJS Client
   Demo Allowing Prediction Only For Registered Users
   Cognito Overview
   Lab: Cognito User Pool Configuration
   Lab: AngularJS Web Client - Invoke Prediction for authorized users
   Lab: Invoke Machine Learning Service From AWS EC2 Instance
   Summary
   Mushroom Classification
   BONUS: Learn Advanced Data Processing Techniques, Cloud Computing and More
   Conclusion


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