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Machine Learning with Python for Everyone
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python
Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.
Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.
Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Kindle Edition
Published July 30, 2019
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Displaying 1 - 2 of 2 reviews
April 25, 2023
Great For All Levels
Wow, this book was truly outstanding! Fenner has done a remarkable job explaining machine learning concepts in Python.
The best part is that it's accessible to everyone, not just tech gurus or data scientists.
The explanations are crystal clear, and I found the book to be incredibly well-structured. The concepts are introduced gradually, and each chapter builds on the last.
The code snippets and visual aids were incredibly helpful, and I could tell that the author put a lot of thought into making the material accessible and engaging.
Even if you're a complete beginner in machine learning, this book is a great place to start. By the end of it, you will feel like you have a solid understanding of the fundamentals and will be able to apply the techniques to your own projects with ease.
Overall, I highly recommend this book to anyone interested in machine learning with Python. It's an outstanding resource, and it deserves a spot on every data enthusiast's bookshelf.
4.7/5
Wow, this book was truly outstanding! Fenner has done a remarkable job explaining machine learning concepts in Python.
The best part is that it's accessible to everyone, not just tech gurus or data scientists.
The explanations are crystal clear, and I found the book to be incredibly well-structured. The concepts are introduced gradually, and each chapter builds on the last.
The code snippets and visual aids were incredibly helpful, and I could tell that the author put a lot of thought into making the material accessible and engaging.
Even if you're a complete beginner in machine learning, this book is a great place to start. By the end of it, you will feel like you have a solid understanding of the fundamentals and will be able to apply the techniques to your own projects with ease.
Overall, I highly recommend this book to anyone interested in machine learning with Python. It's an outstanding resource, and it deserves a spot on every data enthusiast's bookshelf.
4.7/5
February 26, 2020
Machine learning, one of the hottest tech topics of today, is being used more and more. Sometimes as the best tool for the job, other times perhaps as a buzzword that is mainly used as a way to make a product look cooler. However, without knowing what ML is and how it works behind the scenes, it’s very easy to get lost. But this book does a great job in guiding you all the way up from very simple math concepts to some sophisticated machine learning techniques.
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Displaying 1 - 2 of 2 reviews