Free Top 3 Machine Learning Books 📚

 Advances in Financial Machine Learning

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.

Listeners will learn how to structure big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. Listeners become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Graph-Powered Machine Learning 

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.

In Graph-Powered Machine Learning, you will learn:

The lifecycle of a machine learning project

Graphs in big data platforms

Data source modeling using graphs

Graph-based natural language processing, recommendations, and fraud detection techniques

Graph algorithms

Working with Neo4J

Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices.

Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients!

About the Technology

Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications.

About the Audiobook

Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative audiobook, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.

Machine Learning for Absolute Beginners: Python for Data Science, Book 3 

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."

Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add "Machine Learning" to your LinkedIn profile?

Well, hold on there.... Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.

But rather than spend $30-$50 USD on a thick textbook, you may want to listen to this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations.

Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.

New Updated Edition

This new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition.

Disclaimer: If you have passed the "beginner" stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.

In this step-by-step guide, you will learn:

How to download free datasets

What tools and machine learning libraries you need

Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data

Preparing data for analysis, including k-fold Validation

Regression analysis to create trend lines

k-Means Clustering to find new relationships

The basics of Neural Networks

Bias/Variance to improve your machine learning model