Feature Engineering for Machine LearningTransform the variables in your data & build better perfo...

Category: Tutorial

Posted on 2019-12-02, by everest555.


h264, yuv420p, 1280x720 |ENGLISH, aac, 44100 Hz, 2 channels | 9h 47 mn | 3.8 GB
Instructor: Soledad Galli

Transform the variables in your data and build better performing machine learning models

What you'll learn

Learn multiple techniques for missing data imputation
Transform categorical variables into numbers while capturing meaningful information
Learn how to deal with infrequent, rare and unseen categories
Transform skewed variables into Gaussian
Convert numerical variables into discrete
Remove outliers from your variables
Extract meaningful features from dates and time variables
Learn techniques used in organisations worldwide and in data competitions
Increase your repertoire of techniques to preprocess data and build more powerful machine learning models


A Python installation
Jupyter notebook installation
Python coding skills
Some experience with Numpy and Pandas
Familiarity with Machine Learning algorithms
Familiarity with Scikit-Learn


NEW! Updated in November 2019 for the latest software versions, including use of new tools and open-source packages, and additional feature engineering techniques.


Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models.

Who is this course for?

So, you've made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you're probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up. And to make things more complicated, you can't find many consolidated resources about feature engineering. Maybe even just blogs? So you may start to wonder: how are things really done in tech companies?

This course will help you! This is the most comprehensive online course in variable engineering. You will learn a huge variety of engineering techniques used worldwide in different organizations and in data science competitions, to clean and transform your data and variables.

What will you learn?

I have put together a fantastic collection of feature engineering techniques, based on scientific articles, white papers, data science competitions, and of course my own experience as a data scientist.

Specifically, you will learn:

How to impute your missing data

How to encode your categorical variables

How to transform your numerical variables so they meet ML model assumptions

How to convert your numerical variables into discrete intervals

How to remove outliers

How to handle date and time variables

How to work with different time zones

How to handle mixed variables which contain strings and numbers

Throughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an elegant, efficient, and professional manner, using Python, NumPy, Scikit-learn, pandas and a special open-source package that I created especially for this course: Feature- engine.

At the end of the course, you will be able to implement all your feature engineering steps in a single and elegant pipeline, which will allow you to put your predictive models into production with maximum efficiency.

Want to know more? Read on...

In this course, you will initially become acquainted with the most widely used techniques for variable engineering, followed by more advanced and tailored techniques, which capture information while encoding or transforming your variables. You will also find detailed explanations of the various techniques, their advantages, limitations and underlying assumptions and the best programming practices to implement them in Python.

This comprehensive feature engineering course includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

REMEMBER, the course comes with a 30-day money back guarantee, so you can sign up today with no risk. So what are you waiting for? Enrol today, embrace the power of feature engineering and build better machine learning models.
Who this course is for:

Data Scientists who want to get started in pre-processing datasets to build machine learning models
Data Scientists who want to learn more techniques for feature engineering for machine learning
Data Scientist who want to limprove their coding skills and best programming practices for feature engineering
Software engineers, mathematicians and academics switching careers into data science
Data Scientists who want to try different feature engineering techniques on data competitions
Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering



Buy Premium Account for Download With Full Speed:




Links are Interchangeable - No Password - Single Extraction

Sponsored High Speed Downloads
6361 dl's @ 3753 KB/s
Download Now [Full Version]
7135 dl's @ 3162 KB/s
Download Link 1 - Fast Download
9445 dl's @ 2112 KB/s
Download Mirror - Direct Download

Search More...
Feature Engineering for Machine LearningTransform the variables in your data & build better perfo...

Search free ebooks in ebookee.com!

Download this book

No active download links here?
Please check the description for download links if any or do a search to find alternative books.

Related Books


No comments for "Feature Engineering for Machine LearningTransform the variables in your data & build better perfo...".

    Add Your Comments
    1. Download links and password may be in the description section, read description carefully!
    2. Do a search to find mirrors if no download links or dead links.
    Back to Top