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Parallelism of Statistics and Machine Learning

At first glance, machine learning seems to be distant from statistics. However, if we take a deeper look into them, we can draw parallels between both. In this chapter, we will deep dive into the details. Comparisons have been made between linear regression and lasso/ridge regression in order to provide a simple comparison between statistical modeling and machine learning. These are basic models in both worlds and are good to start with.

In this chapter, we will cover the following:

Understanding of statistical parameters and diagnostics
Compensating factors in machine learning models to equate statistical diagnostics
Ridge and lasso regression
Comparison of adjusted R-square with accuracy

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.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Enrico P. Apr 13, 2019

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.

David Oct 22, 2017

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.

Mark Richmond Jun 02, 2020

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.

rajdeep banerjee Sep 26, 2018

Pros:Concise and to the point with panda and R codes.Cons:Needs some more mathematical details for better understanding.

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

What do you get with Print?

Statistics for Machine Learning

Parallelism of Statistics and Machine Learning

Comparison between regression and machine learning models

Compensating factors in machine learning models

Machine learning models - ridge and lasso regression

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