Haroon D. - Python Machine Learning Case Studies [2017, PDF/EPUB, ENG] torrent

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Python Machine Learning Case Studies

Год издания: 2017
Автор: Haroon D.

Издательство: Apress
ISBN: 978-1484228227
Язык: Английский

Формат: PDF/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 204

Описание: Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.
Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs.
By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.

What You Will Learn

- Gain insights into machine learning concepts
- Work on real-world applications of machine learning
- Learn concepts of model selection and optimization
- Get a hands-on overview of Python from a machine learning point of view

Who This Book Is For

Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.
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Contents at a Glance

About the Author .............................................................................. xi
About the Technical Reviewer ........................................................ xiii
Acknowledgments ........................................................................... xv
Introduction ................................................................................... xvii
Chapter 1: Statistics and Probability .............................................. 1
Chapter 2: Regression .................................................................. 45
Chapter 3: Time Series ................................................................. 95
Chapter 4: Clustering .................................................................. 129
Chapter 5: Classification ............................................................ 161
Appendix A: Chart types and when to use them ......................... 197
Index ...................................................................................... 201
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Доп. информация: Исходный код:
https://github.com/Apress/python-ml-case-studies