FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: The HundredPage Machine Learning Book
 Author(s) Andriy Burkov
 Publisher: Andriy Burkov (January 13, 2019); eBook (Released Drafts)
 License(s): "read first, buy later"
 Paperback: 159 pages
 eBook: PDF files
 Language: English
 ISBN10: 199957950X
 ISBN13: 9781999579500
 Share This:
Book Description
Everything you really need to know in Machine Learning in a hundred pages!
This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue. A wonderful book for engineers who want to incorporate ML in their daytoday work without necessarily spending an enormous amount of time going through a formal degree program.
This is the first of its kind "read first, buy later" book. You can find the book online, read it, and then come back to pay for it if you liked the book or found it useful for your work, business or studies.
This book is for:
 a software engineer or a scientist who wants to become a machine learning engineer or a data scientist
 a data scientist trying to stay on the edge of the stateoftheart and deepen their ML expertise
 a manager who wants to feel confident while talking about AI with engineers and product people
 a curious person looking to find out how machine learning works and maybe build something new
 Andriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. Nine years ago, he got a Ph.D. in Artificial Intelligence, and for the last six years, he's been leading a team of machine learning developers at Gartner.
 Machine Learning
 Neural Networks and Deep Learning
 Artificial Intelligence
 Data Analysis and Data Mining
 The HundredPage Machine Learning Book (Andriy Burkov)
 The Mirror Site (1)  PDF
 The Mirror Site (2)  PDF
 The Book Homepage (FAQs, Resources, etc.)

Machine Learning Engineering (Andriy Burkov)
The most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. It embraces the most important thing you need to know about machine learning: mistakes are possible.

The Mechanics of Machine Learning (Terence Parr, et al)
A primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. Focus on just a few powerful models (algorithms) that are extremely effective on real problems,

Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

A Course in Machine Learning (Hal Daume III)
This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

Efficient Learning Machines: Theories, Concepts, and Applications
It weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning, aims to design and create new and more efficient machine learning systems.

Machine Learning Yearning (Andrew Ng)
You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

The Big Book of Machine Learning Use Cases
This howto reference guide provides everything you need  including code samples and notebooks  to start putting Machine Learning to work. It's a collection of technical blogs from industry thought leaders with practical use cases you can leverage today.

Machine Learning from Scratch (Danny Friedman)
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.

Pen and Paper Exercises in Machine Learning (Michael Gutmann)
This is a collection of (mostly) penandpaper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

Reinforcement Learning: An Introduction, Second Edition
It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

Dive into Deep Learning (Aston Zhang, et al.)
This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

Deep Learning for Coders with Fastai and PyTorch
This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

Deep Learning (Ian Goodfellow, et al)
Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudocode for the most important algorithms.

Pattern Recognition and Machine Learning (Christopher Bishop)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

Gaussian Processes for Machine Learning (Carl E. Rasmussen)
This book provides a longneeded systematic and unified treatment of theoretical and practical aspects of Gaussian Processes (GPs) in machine learning. It deals with the supervisedlearning problem for both regression and classification.
:






















