Feature Selection for Machine Learning

• 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

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

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