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- Title: Machine Learning by Andrew Ng
- Author(s): Andrew Ng
- Publisher: Stanford University
- Paperback: N/A
- eBook: PDF (245 pages)
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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This course provides a broad introduction to machine learning and statistical pattern recognition, assumes no prior knowledge and progressively introduces you to the concepts. Remember, the objective is to empower you with knowledge, irrespective of your skill level.
About the Authors- Andrew Ng is a British-born American businessman, computer scientist, investor, and writer. He is focusing on machine learning and AI.[2] As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people.
- Machine Learning
- Deep Learning
- Neural Networks
- Artificial Intelligence
- Data Analysis and Data Mining
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- Machine Learning (Andrew Ng)
- The Mirror Site (1) - PDF
- Machine Learning for Beginners (Anderson Coen)
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Machine Learning Yearning (Andrew Ng)
In this book 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.
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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.
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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,
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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.
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Interpretable Machine Learning: Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.
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Lifelong Machine Learning (Zhiyuan Chen, et al)
This book is an introduction to an advanced Machine Learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. This learning ability is one of the hallmarks of human intelligence.
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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.).
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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.
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The Big Book of Machine Learning Use Cases
This how-to 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.
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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 pseudo-code for the most important algorithms.
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