4.71 out of 5
4.71
1338 reviews on Udemy

Feature Selection for Machine Learning

Select the variables in your data to build simpler, faster and more reliable machine learning models.
Instructor:
Soledad Galli
8,971 students enrolled
English [Auto]
Learn about filter, embedded and wrapper methods for feature selection
Find out about hybdrid methods for feature selection
Select features with Lasso and decision trees
Implement different methods of feature selection with Python
Learn why less (features) is more
Reduce the feature space in a dataset
Build simpler, faster and more reliable machine learning models
Analyse and understand the selected features
Discover feature selection techniques used in data science competitions

Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online.

In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models.

Who is this course for?

You’ve given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables. At this stage you’ve probably realized that many data sets contain an enormous amount of features, and some of them are identical or very similar, some of them are not predictive at all, and for some others it is harder to say.

You wonder how you can go about to find the most predictive features. Which ones are OK to keep and which ones could you do without? You also wonder how to code the methods in a professional manner. Probably you did your online search and found out that there is not much around there about feature selection. So you 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 selection. You will learn a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features.

What will you learn?

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

Specifically, you will learn:

  • How to remove features with low variance

  • How to identify redundant features

  • How to select features based on statistical tests

  • How to select features based on changes in model performance

  • How to find predictive features based on importance attributed by models

  • How to code procedures elegantly and in a professional manner

  • How to leverage the power of existing Python libraries for feature selection

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, Scikit-learn, pandas and mlxtend.

At the end of the course, you will have a variety of tools to select and compare different feature subsets and identify the ones that returns the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production.

This comprehensive feature selection course includes about 70 lectures spanning ~8 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.

In addition, I update the course regularly, to keep up with the Python libraries new releases and include new techniques when they appear.

So what are you waiting for? Enroll today, embrace the power of feature selection and build simpler, faster and more reliable machine learning models.

Introduction

1
Introduction
2
Course Curriculum Overview
3
Course requirements
4
Course Aim
5
Optional: How to approach this course
6
Course Material
7
The code | Jupyter notebooks
8
Presentations covered in this course
9
Download the data sets
10
FAQ: Data Science and Python programming

Feature Selection

1
What is feature selection?
2
Feature selection methods | Overview
3
Filter Methods
4
Wrapper methods
5
Embedded Methods
6
Moving Forward
7
Open-source packages for feature selection

Filter Methods | Basics

1
Constant, quasi constant, and duplicated features – Intro
2
Constant features
3
Quasi-constant features
4
Duplicated features
5
Install Feature-engine
6
Drop constant and quasi-constant with Feature-engine
7
Drop duplicates with Feature-engine

Filter methods | Correlation

1
Correlation - Intro
2
Correlation Feature Selection
3
Correlation procedures to select features
4
Correlation | Notebook demo
5
Basic methods plus Correlation pipeline
6
Correlation with Feature-engine
7
Feature Selection Pipeline with Feature-engine
8
Additional reading resources

Filter methods | Statistical measures

1
Statistical methods – Intro
2
Mutual information
3
Mutual information demo
4
Chi-square
5
Chi-square | Demo
6
Anova
7
Anova | Demo
8
Basic methods + Correlation + Filter with stats pipeline

Filter Methods | Other methods and metrics

1
Filter Methods with other metrics
2
Univariate model performance metrics
3
Univariate model performance metrics | Demo
4
KDD 2009: Select features by target mean encoding
5
KDD 2009: Select features by mean encoding | Demo
6
Univariate model performance with Feature-engine
7
Target Mean Encoding Selection with Feature-engine

Wrapper methods

1
Wrapper methods – Intro
2
MLXtend
3
Step forward feature selection
4
Step forward feature selection | Demo
5
Step backward feature selection
6
Step backward feature selection | Demo
7
Exhaustive search
8
Exhaustive search | Demo

Embedded methods | Linear models

1
Regression Coefficients – Intro
2
Selection by Logistic Regression Coefficients
3
Selection by Linear Regression Coefficients
4
Coefficients change with penalty
5
Basic methods + Correlation + Embedded method using coefficients

Embedded methods – Lasso regularisation

1
Regularisation – Intro
2
Lasso
3
A note on SelectFromModel
4
Basic filter methods + LASSO pipeline

Embedded methods | Trees

1
Feature Selection by Tree importance | Intro
2
Feature Selection by Tree importance | Demo
3
Feature Selection by Tree importance | Recursively
4
Feature selection with decision trees | review

Hybrid feature selection methods

1
Introduction to hybrid methods
2
Feature Shuffling - Intro
3
Shuffling features | Demo
4
Recursive feature elimination - Intro
5
Recursive feature elimination | Demo
6
Recursive feature addition - Intro
7
Recursive feature addition | Demo
8
Feature Shuffling with Feature-engine
9
Recursive feature elimination with Feature-engine
10
Recursive feature addition with Feature-engine

Final section | Next steps

1
THERE IS MORE...
2
Additional reading resources
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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5 hours on-demand video
16 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion

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