Introduction

Welcome to the "Feature Engineering and Model Optimization" course. This intensive 5-day program is designed for data professionals looking to enhance their skills in preparing data and optimizing machine learning models. You'll gain hands-on experience in creating effective features, utilizing advanced techniques for model tuning, and ultimately improving the performance of your predictive models.

Objectives

• Understand the principles of feature engineering and its impact on model performance.
• Learn techniques for selecting and extracting relevant features for various datasets.
• Gain proficiency in model optimization strategies to enhance predictive accuracy.
• Explore tools and libraries that facilitate feature engineering and optimization tasks.
• Apply best practices for deploying efficient and robust machine learning models.

Course Outlines

Day 1: Introduction to Feature Engineering

• Overview of feature engineering and its importance
• Types of features and data preprocessing techniques
• Handling missing data and categorical variables
• Understanding correlations and feature interactions
• Case study: Identifying potential features from raw data

Day 2: Advanced Feature Construction Techniques

• Feature extraction using dimensionality reduction
• Generating features through mathematical transformations
• Using domain knowledge for feature creation
• Feature selection methods: Filter, wrapper, and embedded techniques
• Hands-on exercise: Constructing features from complex datasets

Day 3: Optimizing Machine Learning Models

• Introduction to model optimization and hyperparameter tuning
• Grid search and random search techniques
• Implementing Bayesian optimization for model tuning
• Ensemble methods: Boosting, bagging, and stacking
• Lab session: Tuning models for maximum performance

Day 4: Tools and Libraries for Feature Engineering and Optimization

• Using Python libraries for feature engineering: pandas, numpy, and scikit-learn
• Automated feature engineering with FeatureTools and other tools
• Libraries for model optimization: Hyperopt, Optuna, and Tune
• Understanding and using ML pipelines for efficient processing
• Workshop: Building comprehensive pipelines with Python tools

Day 5: Best Practices and Case Studies

• Review of best practices in feature engineering and model optimization
• Exploring industry case studies of successful applications
• Ethical considerations and data integrity
• Group project: Developing and optimizing a model from scratch
• Course wrap-up and Q&A session