We kick off our Python and machine learning journey with the basic, yet important concepts of machine learning. We will start with what machine learning is about, why we need it, and its evolution over the last few decades. We will then discuss typical machine learning tasks and explore several essential techniques of working with data and working with models. It is a great starting point of the subject and we will learn it in a fun way. Trust me. At the end, we will also set up the software and tools needed in this book.
We will get into details for the topics mentioned:
- What is machine learning and why do we need it?
- A very high level overview of machine learning
- Generalizing with data
- Overfitting and the bias variance trade off
- Cross validation
- Regularization
- Dimensions and features
- Preprocessing, exploration, and feature engineering
- Missing Values
- Label encoding
- One hot encoding
- Scaling
- Polynomial features
- Power transformations
- Binning
- Combining models
- Bagging
- Boosting
- Stacking
- Blending
- Voting and averaging
- Installing software and setting up
- Troubleshooting and asking for help
