• Title: Machine Learning Algorithms
• Author(s) Jason Brownlee
• Publisher: My Data Science Blog
• Paperback: N/A
• eBook: HTML and PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book takes you on an enlightening journey through the fascinating world of machine learning, helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs.

• N/A

• Amazon

• Machine Learning
• Algorithms and Data Structures
• Neural Networks and Depp Learning
• Artificial Intelligence
• Statistics, Mathematical Statistics
• Probability and Stochastic Processe

• Machine Learning Algorithms (Jason Brownlee)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Algorithms for Reinforcement Learning (Csaba Szepesvari)

  This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. It gives a fairly comprehensive catalog of learning problems, describe the core ideas, etc.

• 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.

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  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.

• Understanding Machine Learning: From Theory to Algorithms

  Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

• Distributional Reinforcement Learning (Marc G. Bellemare, et al)

  Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

• Reinforcement Learning and Optimal Control (Dimitri Bertsekas)

  The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable.

• 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.

• 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.

• 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.