Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

by Pratap Dangeti

Build machine learning models with a clear statistical understanding

Key FeaturesLearn about the statistics behind powerful predictive models using p-value, ANOVA, and F-statisticsImplement statistical computations programmatically for supervised and unsupervised learning through K-means clusteringGet to grips with the statistical aspects of machine learning with the help of this example-rich guide to R and PythonBook DescriptionComplex statistics in machine learning worry a lot of developers. Developing an accurate understanding of statistics will help you build robust machine learning models that are optimized for a given problem statement.

This book will teach you everything you need to perform the complex statistical computations required for machine learning. You will learn about the statistics behind supervised learning, unsupervised learning, and reinforcement learning. The book will then take you through real-world examples that discuss the statistical side of machine learning to familiarize you with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more.

By the end of this machine learning book, you’ll be well-versed with the statistics required for machine learning and will be able to apply your new skills to tackle problems related to this technology.

What you will learnGrasp the statistical and machine learning fundamentals necessary to build modelsUnderstand the major differences and parallels between the statistical way and the machine learning way to solve problemsDiscover how to prepare data and feed models using appropriate machine learning algorithms from R and Python packagesAnalyze the results and tune the model appropriately to your own predictive goalsAcquaint yourself with the necessary fundamentals required for building supervised and unsupervised deep learning modelsDelve into reinforcement learning and its application in the artificial intelligence domainWho this book is forThis book is for developers with little to no background in statistics who want to implement machine learning in their systems. Some knowledge of R programming or Python programming will be useful.

Table of ContentsJourney from Statistics to Machine LearningParallelism of Statistics and Machine LearningLogistic Regression vs. Random ForestTree-Based Machine Learning modelsK-Nearest Neighbors & Naive BayesSupport Vector Machines & Neural NetworksRecommendation EnginesUnsupervised LearningReinforcement Learning

Genres: Computer Science, Artificial Intelligence, Programming, Nonfiction

680 pages, Kindle Edition

Published July 21, 2017

Reviews

[An · Rating 3 out of 5]

Maybe it's just my reading style, but I felt I only learned the overview of the topic rather than feeling like I've obtained some deeper understanding. It's still perfectly fine for getting and overview, and again this is perhaps my own deficient learning style, but I would have preferred if it studied a few techniques in much more depth, especially at the algorithmic level.

[Stein Karlsen · Rating 5 out of 5]

Great introduction to many statistical terms and concepts.

[Atanu Shome · Rating 2 out of 5]

It started good. But gradually looses the momentum. Felt like you already need to know many things prior reading it. Less example and description is making it hard to understand.

[Delhi Irc]

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