Feature Selection for Machine Learning

Feature Selection for Machine Learning

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What you will learn

  • Understand different methods of feature selection

  • Implement different methods of feature selection

  • Reduce feature space in a dataset

  • Build simpler, faster and more reliable machine learning models

  • Analyse and understand the selected features

Curriculum

Section 1: Introduction

Section 2: Feature Selection

Section 3: Filter Methods | Basics

Section 4: Filter methods | Correlation

Section 5: Filter methods | Statistical measures

Section 6: Wrapper methods

Section 7: Embedded methods – Lasso regularisation

Section 8: Embedded methods | Linear models

Course Description

From beginner to advanced

Requirements

  • A Python installation
  • Jupyter notebook installation
  • Python coding skills
  • Some experience with Numpy and Pandas
  • Familiarity with Machine Learning algorithms
  • Familiarity with scikit-learn

Description

Learn how to select features and build simpler, faster and more reliable machine learning models.

This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor’s experience as a Data Scientist.

You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set.

The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features. Then it describes more complex techniques that select variables taking into account variable interaction, the feature importance and its interaction with the machine learning algorithm. Finally, it describes specific techniques used in data competitions and the industry. 

The lectures include an explanation of the feature selection technique, the rationale to use it, and the advantages and limitations of the procedure. It also includes full code that you can take home and apply to your own data sets.

This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills.

With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of variable selection. Throughout the course you will use python as your main language.

So what are you waiting for? Enrol today, learn how to select variables for machine learning, and build simpler, faster and more reliable 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