Feature Selection for Machine Learning

Feature Selection for Machine Learning, available at $84.99, has an average rating of 4.58, with 88 lectures, 1 quizzes, based on 2197 reviews, and has 16474 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner Data Scientists who want to understand how to select variables for machine learning or Intermediate Data Scientists who want to level up their experience in feature selection for machine learning or Advanced Data Scientists who want to discover alternative methods for feature selection or Software engineers and academics switching careers into data science or Software engineers and academics stepping into data science or Data analysts who want to level up their skills in data science It is particularly useful for Beginner Data Scientists who want to understand how to select variables for machine learning or Intermediate Data Scientists who want to level up their experience in feature selection for machine learning or Advanced Data Scientists who want to discover alternative methods for feature selection or Software engineers and academics switching careers into data science or Software engineers and academics stepping into data science or Data analysts who want to level up their skills in data science.

Enroll now: Feature Selection for Machine Learning

Summary

Title: Feature Selection for Machine Learning

Price: $84.99

Average Rating: 4.58

Number of Lectures: 88

Number of Quizzes: 1

Number of Published Lectures: 88

Number of Published Quizzes: 1

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 89

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Beginner Data Scientists who want to understand how to select variables for machine learning
Intermediate Data Scientists who want to level up their experience in feature selection for machine learning
Advanced Data Scientists who want to discover alternative methods for feature selection
Software engineers and academics switching careers into data science
Software engineers and academics stepping into data science
Data analysts who want to level up their skills in data science

Target Audiences

Beginner Data Scientists who want to understand how to select variables for machine learning
Intermediate Data Scientists who want to level up their experience in feature selection for machine learning
Advanced Data Scientists who want to discover alternative methods for feature selection
Software engineers and academics switching careers into data science
Software engineers and academics stepping into data science
Data analysts who want to level up their skills in data science

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Curriculum Overview

Lecture 2: Course requirements

Lecture 3: Course Aim

Lecture 4: Optional: How to approach this course

Lecture 5: Course Material

Lecture 6: The code | Jupyter notebooks

Lecture 7: Presentations covered in this course

Lecture 8: Download the data sets

Lecture 9: Resources to learn machine learning skills

Chapter 2: Feature Selection

Lecture 1: What is feature selection?

Lecture 2: Feature selection methods | Overview

Lecture 3: Filter Methods

Lecture 4: Wrapper methods

Lecture 5: Embedded Methods

Lecture 6: Moving Forward

Lecture 7: Open-source packages for feature selection

Chapter 3: Filter Methods | Basics

Lecture 1: Constant, quasi constant, and duplicated features – Intro

Lecture 2: Constant features

Lecture 3: Quasi-constant features

Lecture 4: Duplicated features

Lecture 5: Install Feature-engine

Lecture 6: Drop constant and quasi-constant with Feature-engine

Lecture 7: Drop duplicates with Feature-engine

Chapter 4: Filter methods | Correlation

Lecture 1: Correlation – Intro

Lecture 2: Correlation Feature Selection

Lecture 3: Correlation procedures to select features

Lecture 4: Correlation | Notebook demo

Lecture 5: Basic methods plus Correlation pipeline

Lecture 6: Correlation with Feature-engine

Lecture 7: Feature Selection Pipeline with Feature-engine

Lecture 8: Additional reading resources

Lecture 9: ? Bonus! An eye-opening movie experience! ?

Chapter 5: Filter methods | Statistical measures

Lecture 1: Statistical methods – Intro

Lecture 2: Mutual information

Lecture 3: Mutual information demo

Lecture 4: Chi-square test

Lecture 5: Chi-square | Demo

Lecture 6: Chi-square considerations

Lecture 7: Chi2 – calculating the expected frequencies (Optional)

Lecture 8: Anova

Lecture 9: Anova | Demo

Lecture 10: Select features based of p-values

Lecture 11: Do you want to learn more about stats?

Lecture 12: Basic methods + Correlation + Filter with stats pipeline

Chapter 6: Filter Methods | Other methods and metrics

Lecture 1: Filter Methods with other metrics

Lecture 2: Univariate model performance metrics

Lecture 3: Univariate model performance metrics | Demo

Lecture 4: KDD 2009: Select features by target mean encoding

Lecture 5: KDD 2009: Select features by mean encoding | Demo

Lecture 6: Univariate model performance with Feature-engine

Lecture 7: Target Mean Encoding Selection with Feature-engine

Lecture 8: ? Unveiling the Dark Side of Algorithms: A Captivating Book Recommendation!

Chapter 7: Wrapper methods

Lecture 1: Wrapper methods – Intro

Lecture 2: MLXtend

Lecture 3: Step forward feature selection

Lecture 4: SFS – MLXtend vs Sklearn

Lecture 5: Step forward feature selection | MLXtend

Lecture 6: Step forward feature selection | sklearn

Lecture 7: Step backward feature selection

Lecture 8: Step backward feature selection | MLXtend

Lecture 9: Step backward feature selection | Sklearn

Lecture 10: Exhaustive search

Lecture 11: Exhaustive search | Demo

Chapter 8: Embedded methods | Linear models

Lecture 1: Regression Coefficients – Intro

Lecture 2: Selection by Logistic Regression Coefficients

Lecture 3: Selection by Linear Regression Coefficients

Lecture 4: Coefficients change with penalty

Lecture 5: Basic methods + Correlation + Embedded method using coefficients

Chapter 9: Embedded methods – Lasso regularisation

Lecture 1: Regularisation – Intro

Lecture 2: Lasso

Lecture 3: A note on SelectFromModel

Lecture 4: Basic filter methods + LASSO pipeline

Chapter 10: Embedded methods | Trees

Lecture 1: Feature Selection by Tree importance | Intro

Lecture 2: Feature Selection by Tree importance | Demo

Lecture 3: Feature Selection by Tree importance | Recursively

Lecture 4: Feature selection with decision trees | review

Chapter 11: Hybrid feature selection methods

Lecture 1: Introduction to hybrid methods

Lecture 2: Feature Shuffling – Intro

Lecture 3: Shuffling features | Demo

Lecture 4: Recursive feature elimination – Intro

Lecture 5: Recursive feature elimination | Demo

Lecture 6: Recursive feature addition – Intro

Lecture 7: Recursive feature addition | Demo

Lecture 8: Feature Shuffling with Feature-engine

Lecture 9: Recursive feature elimination with Feature-engine

Lecture 10: Recursive feature addition with Feature-engine

Chapter 12: Final bonus section

Lecture 1: Additional reading resources

Instructors

Soledad Galli
Data scientist | Instructor | Software developer
Train in Data Team
Data scientists | Instructors | Software engineers

Rating Distribution

1 stars: 8 votes
2 stars: 17 votes
3 stars: 150 votes
4 stars: 660 votes
5 stars: 1363 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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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