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

**Author**: Pratap Dangeti

**Publisher**: Packt Publishing

**ISBN-10**: 1788295757

**Year**: 2017

**Pages**: 442 / 438

**Language**: English

**File size**: 16.45 MB

**File format**: PDF

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

Key Characteristics

Learn More about the Data behind Strong predictive models Using p-value, ANOVAF and F- statistics.

Master the statistical aspect of Machine Learning with the Assistance of this example-rich manual to Python.

Sophisticated statistics in Machine Learning stress a whole lot of programmers. Knowing statistics makes it possible to build powerful Machine Learning models which are optimized for a given problem statement. This book will teach you everything it requires to execute complex statistical computations necessary for Machine Learning. You will acquire information on data behind supervised learning, unsupervised learning, reinforcement learning, and much more. Know the real world examples which talk about the statistical side of Machine Learning and familiarize yourself with it. You’ll also design applications for performing tasks like design, parameter fitting, regression, classification, density set, and much more.

From the conclusion of the novel, you’ll have mastered the essential data for Machine Learning and will have the ability to apply your skills to some type of business issue.

Everything you may learn

Know the Statistical and Machine Learning principles necessary to construct models

Know the Significant differences and contrasts between the statistical manner along with the Machine Learning method to resolve issues

Learn how to prepare feed and data versions Using the Proper Machine Learning algorithms from the more-than-adequate R and Python packs

Assess the results and song the model suitably to your predictive targets

Know the concepts of necessary data for