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Machine Learning using Python
This book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts such as decision tree learning, random forest, boosting, recommender systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.
Kindle Edition
Published March 7, 2020
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Displaying 1 - 3 of 3 reviews
October 10, 2020
This is probably the best book for beginners who want to get into ML. This provides a comprehensive introduction to all the popular ML algorithms and also teaches the popular libraries of python which is used for analysis, namely pandas. The book is math-heavy but does not make the person feel overwhelmed with the amount of statistics present (the equations appear scary but once you read the descriptions it becomes easy). There are ready-made code templates printed throughout the book which can be plugged and played in other algorithms. For freshers it is suggested to type out the algorithm as it helps in learning the algorithm and trouble shooting the same.
October 20, 2021
Rating: 3.5
Decent book. I will suggest you use this book only if you are enrolled in some ML course, explanations are not too detailed, so it may not work for self study.
Covers many techniques including forecasting models and text analytics which are usually omitted from ML books. Doesn't covers SVMs though.
This book is actually a collection of tutorials, so not comparable to Bishop/Duda etc in rigor but complements them as you get to see how to implement things.
Decent book. I will suggest you use this book only if you are enrolled in some ML course, explanations are not too detailed, so it may not work for self study.
Covers many techniques including forecasting models and text analytics which are usually omitted from ML books. Doesn't covers SVMs though.
This book is actually a collection of tutorials, so not comparable to Bishop/Duda etc in rigor but complements them as you get to see how to implement things.
October 21, 2019
Looks good as of now.. Can give full review after finishing it... Nice book for beginners
Displaying 1 - 3 of 3 reviews