Feature Selection for Machine Learning
Feature Selection for Machine Learning
Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
Product Brand: Udemy
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Udemy Coupon Code for Feature Selection for Machine Learning Course. Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
Created by Soledad Galli, Train in Data Team | 5.5 hours on-demand video course
Machine Learning Course Overview
Feature Selection for Machine Learning
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.
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.
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
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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
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