This repository provides the notebooks which holds the notes along with the practic codes form the book "Introduction to Machine Learning with Python" by Andreas Mueller and Sarah Guido. You can find details about the book on the O'Reilly website.
"This book is a fantastic, super-practical resourse for anyone who wants to start using machine learning in pyhton- I just wish that it had existed when I started using scikit-learn!" -Hanna Wallach
This book has a additional library mglearn as helper functions to create figures and
datasets. If you get ImportError: No module named mglearn you can try to install mglearn into your python environment. All datasets are included in the repository.
This book is for Machine learnig practitioners looking to implement solution to the real-world ML problems, this book doesn't require any previous knowledge of ML, but it is understood that reader of the book has prior knowledge of linear algebra and probabilities though this book doesn't go indeapth analysis of algoritms.
- Chapter 1: Introduces the fundamental concepts of machine learning and its applications.
- Chapters 2 & 3: Describes the actual machine learning algorithms that are most widely used in practice, and discuss their advantages and shortcomings.
- Chapter 4: Discusses the importance of how we represent data that is processed by machine learning.
- Chapter 5: Covers advanced methods for model evaluation and parameter tuning.
- Chapter 6: Explains the concept of pipelines for chaining models and encapsulating the workflow.
- Chapter 7: Shows how to apply the methods described in earlier chapters to text data, and introduces some text-specific processing techniques.
- Chapter 8: Offers a high-level overview, and includes references to more advanced topics.