English | 2021 | ISBN: 9789388511971 | 531 pages | PDF,EPUB | 12.66 MB
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem.

Key Features
● Develop a Conceptual and Mathematical understanding of Statistics
● Get an overview of Statistical Applications in Python
● Learn how to perform Hypothesis testing in Statistics
● Understand why Statistics is important in Machine Learning
● Learn how to process data in Python
Description
This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning.
What you will learn
● Understand the basics of Statistics
● Get to know more about Descriptive Statistics
● Understand and learn advanced Statistics techniques
● Learn how to apply Statistical concepts in Python
● Understand important Python packages for Statistics and Machine Learning
Who this book is for
This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite.
Table of Contents
1. Introduction to Statistics
2. Descriptive Statistics
3. Probability
4. Random Variables
5. Parameter Estimations
6. Hypothesis Testing
7. Analysis of Variance
8. Regression
9. Non Parametric Statistics
10. Data Analysis using Python
11. Introduction to Machine Learning