Download this PDF book: Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python 1st ed. Edition by Manohar Swamynathan
This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away.
Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.
You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered.
Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation.
All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
What You'll Learn
Examine the fundamentals of Python programming language
Review machine Learning history and evolution
Understand machine learning system development frameworks
Implement supervised/unsupervised/reinforcement learning techniques with examples
Explore fundamental to advanced text mining techniques
Implement various deep learning frameworks
Who This Book Is For
Python developers or data engineers looking to expand their knowledge or career into machine learning area.
Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.
Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning
Introduction :
This book is your practical guide towards novice to master in machine learning with Python in six steps. The six steps path has been designed based on the “Six degrees of separation” theory that states that everyone and everything is a maximum of six steps away.
Note that the theory deals with the quality of connections, rather than their existence. So a great effort has been taken to design eminent, yet simple six steps covering fundamentals to advanced topics gradually that will help a beginner walk his way from no or least knowledge of machine learning in Python to all the way to becoming a master practitioner.
This book is also helpful for current Machine Learning practitioners to learn the advanced topics such as Hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and the basics of reinforcement learning
About the Author
Manohar Swamynathan is a data science practitioner and an avid programmer with over 13 years of experience in various data science related areas that include data warehousing, Business Intelligence (BI), analytical tool development, ad-hoc analysis, predictive modeling, data science product development, consulting, formulating strategy and executing analytics program.
He's had a career covering life cycle of data across different domains, such as US mortgage banking, retail, insurance, and industrial IoT. He has a bachelor's degree with a specialization in physics, mathematics, computers, and a master's degree in project management.
He's currently living in Bengaluru, the Silicon Valley of India, working as Staff Data Scientist with GE Digital, contributing to the next big digital industrial revolution.
Contents:
Introduction
Chapter 1: Step 1 – Getting Started in Python
Chapter 2: Step 2 – Introduction to Machine Learning
Chapter 3: Step 3 – Fundamentals of Machine Learning
Chapter 4: Step 4 – Model Diagnosis and Tuning
Chapter 5: Step 5 – Text Mining and Recommender Systems
Chapter 6: Step 6 – Deep and Reinforcement Learning
Chapter 7: Conclusion
About the book:
Publisher : Apress; 1st ed. edition
Publication date : June 5, 2017
Language : English
Pages : 484
File : PDF, 10MB
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