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Book: Mastering Machine Learning with Python in Six Steps

Book: Mastering #MachineLearning with Python in Six Steps #abdsc

  • This book is your practical guide towards novice to master in machine learning with Python in six steps.
  • So, a great effort has been taken to design an 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 basics of reinforcement learning.
  • The traditional approach of math to machine learning i.e., learning all the mathematic then understanding how to implement them to solve problems need a great deal of time/effort which has proven to be not efficient for working professionals looking to switch careers.
  • This book will serve as a great resource for learning machine learning concepts and implementation techniques for:

A Practical Implementation Guide to Predictive Data Analytics Using Python

Covers basic to advanced topics in an easy step-oriented manner
Concise on theory,…

@analyticbridge: Book: Mastering #MachineLearning with Python in Six Steps #abdsc

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 which 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 an 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 basics of reinforcement learning.

Each topic has two parts, the first part will cover the theoretical concepts and the second part will cover practical implementation with different Python packages. The traditional approach of math to machine learning i.e., learning all the mathematic then understanding how to implement them to solve problems need a great deal of time/effort which has proven to be not efficient for working professionals looking to switch careers. Hence the focus in this book has been more on simplification, such that the theory/math behind algorithms have been covered only to extend required to get you started.

I recommend you to work with the book instead of reading it. Real learning goes on only through active participation. Hence, all the code presented in the book are available in the form of iPython notebooks to enable you to try out these examples yourselves and extend them to your advantage or interest as required later.

This book will serve as a great resource for learning machine learning concepts and implementation techniques for:

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Book: Mastering Machine Learning with Python in Six Steps

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