Feature Selection for Machine Learning

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Created by: Soledad Galli
Data scientist | Instructor | Software developer

Created by: Train in Data Team
Data scientists | Instructors | Software engineers

What Will I 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

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

Target audience

• 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