The Hitchhiker's Guide to Machine Learning Algorithms: 100+ Machine Learning Algorithms Explained So Simply Even a Human Can Understand

by Devin Schumacher, Francis La Bounty Jr., Devanshu Mahapatra

Hello humans & welcome to the world of machines! Specifically, machine learning & algorithms. We are about to embark on an exciting adventure through the vast and varied landscape of algorithms that power the cutting-edge field of artificial intelligence. Machine learning is changing the world as we know it. From predicting stock market trends and diagnosing diseases to powering the virtual assistants in our smartphones and enabling self-driving cars, and picking up the slack on your online dating conversations. What makes this book unique is its structure and depth. With 100 chapters, each dedicated to a different machine learning concept, this book is designed to be your ultimate guide to the world of machine learning algorithms. Whether you are a student, a data science professional, or someone curious about machine learning, this book aims to provide a comprehensive overview that is both accessible and in-depth. The algorithms covered in this book span various categories Each chapter is designed as a standalone introduction to its respective algorithm. This means you can start from any chapter that catches your interest or proceed sequentially. Along with the theory, practical examples, applications, and insights into how these algorithms work under the hood are provided. This book is not just an academic endeavor but a bridge that connects theory with practical real-world applications. It's an invitation to explore, learn, and harness the power of algorithms to solve complex problems and make informed decisions. Fasten your seat belts as we dive into the mesmerizing world of machine learning algorithms. Whether you are looking to expand your knowledge, seeking inspiration, or in pursuit of technical mastery, this book should sit on your coffee table and make you look intelligent in front of all invited (and uninvited) guests.

Genres: Artificial Intelligence

679 pages, Kindle Edition

Published July 25, 2023

About the author

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SUMMARY:

Artificial Intelligence is HERE! ... And there's not much "artificial" about it.

Used Correctly, AI Will Help You:
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Live More: Automate menial and trivial tasks, you can create more balance in your life and spend more time on what matters most, such as family, hobbies, and personal development. This can increase overall happiness and fulfillment.

Enjoy More: More time means more pursuit of your passions & interests, ultimately leading to greater fulfillment and happiness.

... And that just scratches the surface.


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Reviews

[Dan Glenco · Rating 5 out of 5]

The Hitchhiker's Guide to Machine Learning Algorithms is an invaluable resource for both beginners and experienced practitioners. I appreciate how the author makes difficult concepts understandable without sacrificing depth. The book strikes a perfect balance between theory and hands-on application. I particularly enjoyed the code snippets and data visualizations, which clarified the inner workings of each algorithm. This guide saved me countless hours of searching through scattered information online. Whether you're a data scientist or a curious learner, this book is an essential addition to your ML library.

[Leslie Gove · Rating 5 out of 5]

The Hitchhiker's Guide to Machine Learning Algorithms is a must-have for anyone diving into the world of ML. The book's clarity and concise explanations make complex algorithms seem approachable. As a beginner, I found the step-by-step breakdown of each algorithm incredibly helpful. The practical examples and real-world applications gave me the confidence to implement these algorithms in my own projects. It's a well-organized and comprehensive guide that I will continue to refer back to as I progress in my ML journey. Highly recommended!

[Kimberly Garza · Rating 5 out of 5]

I cannot praise The Hitchhiker's Guide to Machine Learning Algorithms enough. It's a comprehensive and well-structured reference that caters to all skill levels. Even as an experienced ML engineer, I found insightful nuggets of information that improved my understanding of algorithms. The book's clear examples and practical advice help bridge the gap between theory and implementation. I've recommended this guide to my colleagues and students alike, as it truly accelerates the learning process. Kudos to the author for creating such a gem in the realm of machine learning literature.

[Adam Ferrari · Rating 5 out of 5]

wow! nice book for Machine Learning Algorithms.

[James Leader · Rating 5 out of 5]

what a book for machine learning.....

[Clarence Willoughby · Rating 5 out of 5]

Excellent beginner's guide! Concise explanations and practical examples.

[Davi Sousa · Rating 5 out of 5]

A comprehensive overview of ML algorithms. Great reference material.

[Roy Barnett · Rating 5 out of 5]

Clear and well-organized content. Highly recommended for beginners.

[Douglas Clayton · Rating 5 out of 5]

Informative book with real-world applications. Easy to follow.

[Judd Shaw · Rating 5 out of 5]

Perfect quick guide for refreshing ML concepts. Loved it!

[Joanne Lombardi · Rating 5 out of 5]

A valuable addition to my ML library. Precise and effective.

[Mike Stovall · Rating 5 out of 5]

The go-to guide for algorithm understanding. Good job!

[Gustavo Rhodes · Rating 5 out of 5]

Simplified explanations make ML accessible to all.

[Lawrence Scott · Rating 5 out of 5]

An excellent resource for ML enthusiasts. Must-have!

[Paul Hall · Rating 5 out of 5]

Great intro for developers venturing into ML territory.

[Allen Rodriguez · Rating 5 out of 5]

A good starting point for exploring ML. Good examples!!!!!!!!!!!!!

[Brian Vogt · Rating 5 out of 5]

Overall, a satisfactory guide. Covers key algorithms well.

[Paul Roper · Rating 5 out of 5]

Great for a quick ML overview. Good for beginners.

[Dr. Chris · Rating 5 out of 5]

wow! An exceptional ML guide, perfect for beginners!

[Frederick Cervantes · Rating 5 out of 5]

Clear and concise explanations. Highly informative!

[Kathy Lambert · Rating 5 out of 5]

Practical examples make learning enjoyable.

[Frank Peterson · Rating 5 out of 5]

I think this is the best resource for understanding algorithms ❤ it.

[Eddie Taylor · Rating 5 out of 5]

My ML abilities have significantly improved.

[Nancy Jones · Rating 5 out of 5]

Well-structured and easy to follow

[Jerome A. · Rating 5 out of 5]

Exceeded my expectations. Impressive content.

[William Crow · Rating 5 out of 5]

A gem among ML guides! The practical insights and clear explanations have taken my skills to the next level.

[John Clason · Rating 5 out of 5]

An exceptional ML reference that should be on every data scientist's bookshelf. It's simply outstanding!

[Trysh Sutton · Rating 5 out of 5]

A well-crafted guide that made me fall in love with machine learning all over again. Kudos to the author!

[Kevin Johnston · Rating 5 out of 5]

A masterful guide to machine learning algorithms. Empowered me to tackle ML projects confidently.

[Avram Gonzales · Rating 5 out of 5]

The author's knack for explaining ML algorithms in a straightforward manner is commendable