Learn Python For Data Science & Machine Learning From A-Z

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Posted on 2022-09-24, by avaxshare.

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Learn Python For Data Science & Machine Learning From A-Z
Last updated 10/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.40 GB | Duration: 22h 54m

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Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!
What you'll learn
Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
Learn data cleaning, processing, wrangling and manipulation
How to create resume and land your first job as a Data Scientist
How to use Python for Data Science
How to write complex Python programs for practical industry scenarios
Learn Plotting in Python (graphs, charts, plots, histograms etc)
Learn to use NumPy for Numerical Data
Machine Learning and it's various practical applications
Supervised vs Unsupervised Machine Learning
Learn Regression, Classification, Clustering and Sci-kit learn
Machine Learning Concepts and Algorithms
K-Means Clustering
Use Python to clean, analyze, and visualize data
Building Custom Data Solutions
Statistics for Data Science
Probability and Hypothesis Testing
Requirements
Students should have basic computer skills
Students would benefit from having prior Python Experience but not necessary
Description
Learn Python for Data Science & Machine Learning from A-ZIn this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +NumPy - A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.Pandas - A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you'd find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.We understand that theory is important to build a solid foundation, we understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.Together we're going to give you the foundational education that you need to know not just on how to write quote in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.The course covers 5 main areas:1: PYTHON FOR DS+ML COURSE INTROThis intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.Intro to Data Science + Machine Learning with PythonData Science Industry and MarketplaceData Science Job OpportunitiesHow To Get a Data Science JobMachine Learning Concepts & Algorithms2: PYTHON DATA ANALYSIS/VISUALIZATIONThis section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.Python Crash CourseNumPy Data AnalysisPandas Data Analysis3: MATHEMATICS FOR DATA SCIENCEThis section gives you a full introduction to the mathematics for data science such as statistics and probability. Descriptive Statistics Measure of VariabilityInferential StatisticsProbabilityHypothesis Testing4: MACHINE LEARNINGThis section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.Intro to Machine LearningData PreprocessingLinear RegressionLogistic RegressionK-Nearest NeighborsDecision TreesEnsemble LearningSupport Vector MachinesK-Means ClusteringPCA5: STARTING A DATA SCIENCE CAREERThis section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.Creating a ResumeCreating a Cover LetterPersonal BrandingFreelancing + Freelance websitesImportance of Having a WebsiteNetworkingBy the end of the course you'll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Overview
Section 1: Introduction
Lecture 1 Who is This Course For?
Lecture 2 Data Science + Machine Learning Marketplace
Lecture 3 Data Science Job Opportunities
Lecture 4 Data Science Job Roles
Lecture 5 What is a Data Scientist?
Lecture 6 How To Get a Data Science Job
Lecture 7 Data Science Projects Overview
Section 2: Data Science & Machine Learning Concepts
Lecture 8 Why We Use Python?
Lecture 9 What is Data Science?
Lecture 10 What is Machine Learning?
Lecture 11 Machine Learning Concepts & Algorithms
Lecture 12 What is Deep Learning?
Lecture 13 Machine Learning vs Deep Learning
Section 3: Python For Data Science
Lecture 14 What is Programming?
Lecture 15 Why Python for Data Science?
Lecture 16 What is Jupyter?
Lecture 17 What is Google Colab?
Lecture 18 Python Variables, Booleans and None
Lecture 19 Getting Started with Google Colab
Lecture 20 Python Operators
Lecture 21 Python Numbers & Booleans
Lecture 22 Python Strings
Lecture 23 Python Conditional Statements
Lecture 24 Python For Loops and While Loops
Lecture 25 Python Lists
Lecture 26 More about Lists
Lecture 27 Python Tuples
Lecture 28 Python Dictionaries
Lecture 29 Python Sets
Lecture 30 Compound Data Types & When to use each one?
Lecture 31 Python Functions
Lecture 32 Object Oriented Programming in Python
Section 4: Statistics for Data Science
Lecture 33 Intro To Statistics
Lecture 34 Descriptive Statistics
Lecture 35 Measure of Variability
Lecture 36 Measure of Variability Continued
Lecture 37 Measures of Variable Relationship
Lecture 38 Inferential Statistics
Lecture 39 Measure of Asymmetry
Lecture 40 Sampling Distribution
Section 5: Probability & Hypothesis Testing
Lecture 41 What Exactly is Probability?
Lecture 42 Expected Values
Lecture 43 Relative Frequency
Lecture 44 Hypothesis Testing Overview
Section 6: NumPy Data Analysis
Lecture 45 Intro NumPy Array Data Types
Lecture 46 NumPy Arrays
Lecture 47 NumPy Arrays Basics
Lecture 48 NumPy Array Indexing
Lecture 49 NumPy Array Computations
Lecture 50 Broadcasting
Section 7: Pandas Data Analysis
Lecture 51 Introduction to Pandas
Lecture 52 Introduction to Pandas Continued
Section 8: Python Data Visualization
Lecture 53 Data Visualization Overview
Lecture 54 Different Data Visualization Libraries in Python
Lecture 55 Python Data Visualization Implementation
Section 9: Machine Learning
Lecture 56 Introduction To Machine Learning
Section 10: Data Loading & Exploration
Lecture 57 Exploratory Data Analysis
Section 11: Data Cleaning
Lecture 58 Feature Scaling
Lecture 59 Data Cleaning
Section 12: Feature Selecting and Engineering
Lecture 60 Feature Engineering
Section 13: Linear and Logistic Regression
Lecture 61 Linear Regression Intro
Lecture 62 Gradient Descent
Lecture 63 Linear Regression + Correlation Methods
Lecture 64 Linear Regression Implementation
Lecture 65 Logistic Regression
Section 14: K Nearest Neighbors
Lecture 66 KNN Overview
Lecture 67 parametric vs non-parametric models
Lecture 68 EDA on Iris Dataset
Lecture 69 The KNN Intuition
Lecture 70 Implement the KNN algorithm from scratch
Lecture 71 Compare the result with the sklearn library
Lecture 72 Hyperparameter tuning using the cross-validation
Lecture 73 The decision boundary visualization
Lecture 74 Manhattan vs Euclidean Distance
Lecture 75 Feature scaling in KNN
Lecture 76 Curse of dimensionality
Lecture 77 KNN use cases
Lecture 78 KNN pros and cons
Section 15: Decision Trees
Lecture 79 Decision Trees Section Overview
Lecture 80 EDA on Adult Dataset
Lecture 81 What is Entropy and Information Gain?
Lecture 82 The Decision Tree ID3 algorithm from scratch Part 1
Lecture 83 The Decision Tree ID3 algorithm from scratch Part 2
Lecture 84 The Decision Tree ID3 algorithm from scratch Part 3
Lecture 85 ID3 - Putting Everything Together
Lecture 86 Evaluating our ID3 implementation
Lecture 87 Compare with Sklearn implementation
Lecture 88 Visualizing the tree
Lecture 89 Plot the features importance
Lecture 90 Decision Trees Hyper-parameters
Lecture 91 Pruning
Lecture 92[Optional] Gain Ration
Lecture 93 Decision Trees Pros and Cons
Lecture 94[Project] Predict whether income exceeds $50K/yr - Overview
Section 16: Ensemble Learning and Random Forests
Lecture 95 Ensemble Learning Section Overview
Lecture 96 What is Ensemble Learning?
Lecture 97 What is Bootstrap Sampling?
Lecture 98 What is Bagging?
Lecture 99 Out-of-Bag Error (OOB Error)
Lecture 100 Implementing Random Forests from scratch Part 1
Lecture 101 Implementing Random Forests from scratch Part 2
Lecture 102 Compare with sklearn implementation
Lecture 103 Random Forests Hyper-Parameters
Lecture 104 Random Forests Pros and Cons
Lecture 105 What is Boosting?
Lecture 106 AdaBoost Part 1
Lecture 107 AdaBoost Part 2
Section 17: Support Vector Machines
Lecture 108 SVM Outline
Lecture 109 SVM intuition
Lecture 110 Hard vs Soft Margins
Lecture 111 C hyper-parameter
Lecture 112 Kernel Trick
Lecture 113 SVM - Kernel Types
Lecture 114 SVM with Linear Dataset (Iris)
Lecture 115 SVM with Non-linear Dataset
Lecture 116 SVM with Regression
Lecture 117[Project] Voice Gender Recognition using SVM
Section 18: K-means
Lecture 118 Unsupervised Machine Learning Intro
Lecture 119 Unsupervised Machine Learning Continued
Lecture 120 Data Standardization
Section 19: PCA
Lecture 121 PCA Section Overview
Lecture 122 What is PCA?
Lecture 123 PCA Drawbacks
Lecture 124 PCA Algorithm Steps (Mathematics)
Lecture 125 Covariance Matrix vs SVD
Lecture 126 PCA - Main Applications
Lecture 127 PCA - Image Compression
Lecture 128 PCA Data Preprocessing
Lecture 129 PCA - Biplot and the Screen Plot
Lecture 130 PCA - Feature Scaling and Screen Plot
Lecture 131 PCA - Supervised vs Unsupervised
Lecture 132 PCA - Visualization
Section 20: Data Science Career
Lecture 133 Creating A Data Science Resume
Lecture 134 Data Science Cover Letter
Lecture 135 How to Contact Recruiters
Lecture 136 Getting Started with Freelancing
Lecture 137 Top Freelance Websites
Lecture 138 Personal Branding
Lecture 139 Networking Do's and Don'ts
Lecture 140 Importance of a Website
Students who want to learn about Python for Data Science & Machine Learning
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