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Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version's approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.
You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.
Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement 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
Understand machine learning development and frameworks
Assess model diagnosis and tuning in machine learning
Examine text mining, natuarl language processing (NLP), and recommender systems
Review reinforcement learning and CNN
Who This Book Is For
Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.
Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement 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
Understand machine learning development and frameworks
Assess model diagnosis and tuning in machine learning
Examine text mining, natuarl language processing (NLP), and recommender systems
Review reinforcement learning and CNN
Who This Book Is For
Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
- GenresComputer Science
344 pages, Paperback
Published November 1, 2019
16 people are currently reading
43 people want to read
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Displaying 1 - 4 of 4 reviews
June 24, 2017
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.
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.
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March 16, 2020In 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.
March 16, 2020
The content is well organized. easy to read and understand the basics of ML algorithms and how to implement it using popular ML frameworks.
July 1, 2017
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.
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.
Displaying 1 - 4 of 4 reviews