Description:

A practical guide that will help you understand the Statistical Foundations of any Machine Learning ProblemÊKEY FEATURESÊ_ 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 PythonÊDESCRIPTIONÊÊThis 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.Ê 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.ÊWHAT YOU WILLÊ LEARNÊÊ_ 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 LearningÊWHO THIS BOOK IS FORÊThis 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 CONTENTSÊ1. Introduction to Statistics2. Descriptive Statistics3. Probability4. Random Variables5. Parameter Estimations6. Hypothesis Testing7. Analysis of Variance8. Regression9. Non Parametric Statistics10. Data Analysis using Python11. Introduction to Machine Learning