Mastering Machine Learning with Python in Six Steps

Mastering Machine Learning with Python in Six Steps
Mastering Machine Learning with Python in Six Steps is published by in June 2017. This book has 358 pages in English, ISBN-13 978-1484228654.

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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