Название: Statistical Methods for Machine Learning: Discover how to Transform Data into Knowledge with Python Автор: Jason Brownlee
Издательство: Machine Learning Mastery Pty Ltd.
Год: 2019
Страниц: 291
Язык: английский
Формат: pdf (true), djvu
Размер: 10.1 MB
Statistics is a pillar of Machine Learning (ML). You cannot develop a deep understanding and application of Machine Learning without it.
Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know.
Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to Machine Learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.
Welcome to Statistical Methods for Machine Learning. The field of statistics is hundreds of years old and statistical methods are central to working through predictive modeling problems with machine learning. Statistical methods refer to a range of techniques from simple summary statistics intended to help better understand data, to statistical hypothesis tests and estimation statistics that can be used to interpret the results of experiments and predictions from models. I designed this book to teach you step-by-step the basics of statistical methods with concrete and executable examples in Python.
Who Is This Book For?
Before we get started, let’s make sure you are in the right place. This book is for developers that may know some applied machine learning. Maybe you know how to work through a predictive modeling problem end-to-end, or at least most of the main steps, with popular tools. The lessons in this book do assume a few things about you, such as:
- You know your way around basic Python for programming.
- You may know some basic NumPy for array manipulation.
- You want to learn statistical methods to deepen your understanding and application of machine learning.
This guide was written in the top-down and results-first machine learning style that you’re used to from Machine Learning Mastery.
This book is not intended to be read passively or be placed in a folder as a reference text. It is a playbook, a workbook, and a guidebook intended for you to learn by doing and then apply your new understanding with working Python examples.
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