We kick off our Python and machine learning journey with the basic, yet important, concepts of machine learning. We'll start with what machine learning is about, why we need it, and its evolution over a few decades. We'll then discuss typical machine learning tasks and explore several essential techniques of working with data and working with models. It's a great starting point for the subject and we'll learn it in a fun way. Trust me. At the end, we'll also set up the software and tools needed for this book.

We'll go into detail on the following topics:

• Overview of machine learning and the importance of machine learning
• The core of machine learning—generalizing with data
• Overfitting
• Underfitting
• Bias variance trade-off
• Techniques to avoid overfitting
• Techniques for data preprocessing
• Techniques for feature engineering
• Techniques for model aggregation
• Software installing
• Python package setup