Intro to ML book

Are you ready to kickstart your journey into the world of Machine Learning? This book is a great guide to getting to know the fundamentals of machine learning. Whether you're a beginner or looking to deepen your understanding, this book has got you covered.

This is a beta version of the book, more and better content coming soon!

View a sample at https://beta.introtoml.com/en/book.html

Chapter overview

• Introduction to ML: Get to know the fundamentals of machine learning and what it is about, why we use it, and what are the benefits and drawbacks of using it.
• Scikit-Learn: This chapter is a quick overview of the Scikit-Learn library, from its philosophy, installation, and the basics of how to use it.
• Before Building Your First Model: There are some things you need to know before starting to build your first model, this chapter is a quick overview of the most important things you need to consider.
• Feature Engineering: With code examples, you will learn some of the most common feature engineering techniques.
• Model Evaluation: Learn how to evaluate your models and choose the best one for your needs.
• Best Practices: Learn some of the best practices for building machine learning models, such as how to integrate all the feature transformations into a single pipeline and how to best store a model.
• Final Considerations: A quick overview of the most important things you need to consider when building machine learning models.

About the book

This book was part of the notes for the courses 'Introduction to Machine Learning' and 'Machine Learning with Scikit-Learn' at Código Facilito, which now have 15K students. It is meant to be a guide for anyone who wants to learn about machine learning. It is mainly aimed at people who want to take their first steps into the field of AI and ML, but it is also a good reference for people who want to refresh their knowledge.

What do you get?

• Updates, forever: I keep this book updated with new content, examples and best practices as the field changes, and I learn more about what is important and what is not.
• Online version: You can read the book online, wherever you are and in a format that is easy to read on any device.
• Electronic PDF: You can also get a PDF version of the book that you can read offline, or print it if you prefer.
• Real-world examples (coming soon): The book contains real-world examples, so you can see how ML is used in practice.
• Executable notebooks: The code for the book is available as executable notebooks, so you can run it and experiment with the code.
• Spanish version: I know many people prefer to read books in their native language, so I'm working on a Spanish version of the book. You will get it for free too!

About the author

Hi, I am Antonio Feregrino a Senior MLOps engineer who started his career as a software engineer and then transitioned into Data Science with a Master's degree in Data Science from the University of Glasgow. I have worked as a Data Scientist building different kinds models to deal with web-scraped data, a few years ago I decided to take my career back to the software development side and thus I became a MLOps engineer. I have a passion for teaching, presenting and writing about software and machine learning, making complex concepts accessible to learners of all levels and for that reason, I have prepared this book.