Feature selection for machine learning in Python

In this practicalcourse, we are going to focus on the feature selection approaches for machine learning using Python programming language.

Selecting the best set of features is crucial for the success of a machine learning project. Too many features will not make the model learn the information properly while using a few features won’t carry enough information.

Each model has its own needs regarding the features to learn from, so it’s important to select them properly.

If you want a stableand efficientmodel, selecting the right number of variables is one of the most important steps in your data science pipeline.

With this course, you are going to learn:

1. Feature selection for regression models

2. Feature selection for classification models

3. Recursive Feature Elimination

4. Recursive Feature Elimination with cross-validation

All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.

This course is part of my Supervised Machine Learning in Python online course, so you’ll find some lessons that are already included in the more extensive course.

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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!