Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python

by Manohar Swamynathan

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.

Genres: Computer Science

374 pages, Paperback

Published June 7, 2017

Reviews

[Jojo Moolayil · Rating 5 out of 5]

I was involved in the Technical Review for this book as a freelancer and I totally loved this book.
Manohar's book is one among the best available books for a Data Science enthusiast to get started and learn Machine Learning concepts with Python. The book covers a wide array of topics in Machine Learning in the right depth and breadth. The author has done an amazing work in intuitively organising the content flow in the increasing order of complexity in a lucid language with simple, detailed and easy to understand examples.
This is one of the most comprehensive guide for learning Machine Learning and I would highly recommend any data science enthusiast to grab a copy and start learning.

[Oluwaseun Boluro-Ajayi]

In all sincerity this is a very good book if you want to get started in Machine learning and feel it is to technical. This book focuses more on the application and gives you a good grasp on the basic concepts from which you can then build up from. The organisation of contents enables easy flowing from once concept to another.

[Manivassakam Mouttayen · Rating 4 out of 5]

The content is well organized. easy to read and understand the basics of ML algorithms and how to implement it using popular ML frameworks.

[Manjunath H · Rating 5 out of 5]

A must have for new machine learning enthusiast.
I'm a beginner to Python and machine learning. I loved the flow of topics, breadth and depth covered. The illustrations are useful to understand the concept without getting too much into the mathematics behind.