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Feature Selection for Machine Learning

(12 customer reviews)
Product is rated as #21 in category Development

What you’ll 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

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.

Who this course is for:

  • 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

12 reviews for Feature Selection for Machine Learning

4.8 out of 5
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  1. Jose Manuel Martin Ayala

    Presenta de forma ordenada y comprensible todos los métodos que se pueden utilizar para seleccionar las variables, con sus pros y contras, de forma práctica y reproducible. Merece la pena hacer este curso y tenerlo para consultarlo. Se agradecería mucho que también lo tuviéramos en español. Gracias por el curso Soledad.

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  2. Alok Nagar

    Marvelous course…thanks for making this complicated subject so easy to understand

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  3. Leonardo Barbosa

    Great course. Really good review of various methos for feature selection. I good refresher on those that I have looked into, and a good intro to some that I have yet to explore.

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  4. Keith Koch

    Tremendous amount of detail and options were presented for feature selection. These Jupyter notebooks will come in very handy!

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  5. Yeji Seoung

    I was able to learn about a lot of different methods of feature selection. She taught pros and cons for each method. It was easy to follow every example notebooks and to understand all methods.
    Thank you!

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  6. Omar Giron

    Excelente curso, se explica muy bien los conceptos, tiene una gran cantidad de notebooks ¡¡es algo super!!.

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  7. Dr. Rajeev Kumar Gupta

    Best course for the feature selection. Qualitative contents

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  8. Abzalbek Ulasbekov

    I have learned the essence of methods, but it will be better if lecturer will explain code step by step with implementation, I mean live. Thank you!

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  9. Rajesh S

    amazing course learned many new procedures of feature selection.

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  10. Shyam Sundar Domakonda

    Great Course for Feature Selection. Presentations related to concepts was very well explained. Coding part was explained in easy and in simple way to understand .
    Thank you ma’am for this wonderful course.

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  11. Antonio García Girón

    A very useful course for people, like me, who is starting in the Data Science world. In the bootcamps and courses to learn Python for Machine Learning usually the focus is on the algorithms and cleaning the datasets, and sometimes is even more important to know how to select the best features. Fully recommended!

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  12. Lucas Rocha

    The quality of notebooks on this course is great. Well explained. Now I`m aware of the basic techniques of feature selections in Machine Learning

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    Feature Selection for Machine Learning
    Feature Selection for Machine Learning


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