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Statistics for Machine Learning
Statistics for Machine Learning

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

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Statistics for Machine Learning

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Key benefits

  • Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
  • Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
  • Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.

Description

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself 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 the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.

Who is this book for?

This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.

What you will learn

  • Understand the statistical and machine learning fundamentals necessary to
  • build models
  • Understand the major differences and parallels between the statistical way and the machine learning way to solve problems
  • Learn how to prepare data and feed models by using the appropriate machine learning algorithms from the more-than-adequate R and Python packages
  • Analyze the results and tune the model appropriately to your own predictive goals
  • Understand the concepts of the statistics required for machine learning
  • Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models
  • Learn reinforcement learning and its application in the field of artificial intelligence domain

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 21, 2017
Length: 442 pages
Edition : 1st
Language : English
ISBN-13 : 9781788295758
Category :
Languages :

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Product Details

Publication date : Jul 21, 2017
Length: 442 pages
Edition : 1st
Language : English
ISBN-13 : 9781788295758
Category :
Languages :

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Frequently bought together


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Table of Contents

9 Chapters
Journey from Statistics to Machine Learning Chevron down icon Chevron up icon
Parallelism of Statistics and Machine Learning Chevron down icon Chevron up icon
Logistic Regression Versus Random Forest Chevron down icon Chevron up icon
Tree-Based Machine Learning Models Chevron down icon Chevron up icon
K-Nearest Neighbors and Naive Bayes Chevron down icon Chevron up icon
Support Vector Machines and Neural Networks Chevron down icon Chevron up icon
Recommendation Engines Chevron down icon Chevron up icon
Unsupervised Learning Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(6 Ratings)
5 star 50%
4 star 0%
3 star 33.3%
2 star 0%
1 star 16.7%
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Amazon Customer Nov 20, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Useful
Amazon Verified review Amazon
Enrico P. Apr 13, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Un ottimo libro per chi vuole approfondire la parte statistica del Machine Learning (molto approfondito) e del Deep Learning (in maniera superficiale) ricco di esempi e di codice in Python e R; adatto per chi è ad una seconda fase di approfondimento delle stesse tematiche.
Amazon Verified review Amazon
David Oct 22, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Just finished this book as a primer for my machine learning course this week. It is an excellent resource, that has both Python and R examples throughout the text. Some examples are only in Python when R has no library or functionality for the example. It is easy to read and a determined student could read it in about 4 hours. The examples used are great at illustrating the different algorithms. Great book and glad I was able to find it this past week before I dive back into the classroom.
Amazon Verified review Amazon
Mark Richmond Jun 02, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I find this book to be incredibly frustrating. It's a good book on the whole, a lot of very good and useful information is presented and the author clearly knows what he's talking about. However, the editing is unbelievably bad. All the figures and even the equations are poor quality, they would not be accepted in a journal so why is this quality of figure ok in a book? The figures are so bad that it's often hard to see what the author is even talking about. The author sometimes mentions the colours in a figure, but all the figures are black and white! The author clearly isn't a native English speaker, this is fine of course, but someone who is really should have corrected this before it was published. There are so many sentences which simply don't make sense. Some of the topics are hard enough to understand without having to decipher what sentences are supposed to say.On the whole, now that I have the book I think it's worth persisting to get the value from it because there's a lot of good stuff in here. But I find it hard to recommend that anyone buy this without these issues being fixed.
Amazon Verified review Amazon
rajdeep banerjee Sep 26, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Pros:Concise and to the point with panda and R codes.Cons:Needs some more mathematical details for better understanding.
Amazon Verified review Amazon
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