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Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.

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.

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"This book is densely packed with information about Python programming features and approaches to machine learning ... . if you are the kind of person who refers to documentation or written explanations only as a last resort, and prefers to work through code samples in order to understand something, then this is the perfect book for you." (Computing Reviews, October, 2017)

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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.

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