Yuxi - Python machine learning by example : the easiest way to get into machine learning
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BIRMINGHAM - MUMBAI
Author Yuxi (Hayden) Liu | Copy Editor Safis Editing |
Reviewer Alberto Boschetti | Project Coordinator Nidhi Joshi |
Commissioning Editor Veena Pagare | Proofreader Safis Editing |
Acquisition Editor Tushar Gupta | Indexer Tejal Daruwale Soni |
Content Development Editor Aishwarya Pandere | Graphics Tania Dutta |
Technical Editor Prasad Ramesh | Production Coordinator Aparna Bhagat |
Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast.
Alberto Boschetti is a data scientist with strong expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges daily, spanning across natural language processing (NLP), machine learning, and distributed processing. He is very passionate about his job and always tries to be updated on the latest developments of data science technologies, attending meetups, conferences, and other events. He is the author of Python Data Science Essentials, Regression Analysis with Python, and Large Scale Machine Learning with Python, all published by Packt.
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Copyright 2017 Packt Publishing
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Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
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First published: May 2017
Production reference: 1290517
ISBN 978-1-78355-311-2
www.packtpub.com
Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.
, Getting Started with Python and Machine Learning, is the starting point for someone who is looking forward to enter the field of ML with Python. You will get familiar with the basics of Python and ML in this chapter and set up the software on your machine.
, Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms, explains important concepts such as getting the data, its features, and pre-processing. It also covers the dimension reduction technique, principal component analysis, and the k-nearest neighbors algorithm.
, Spam Email Detection with Naive Bayes, covers classification, naive Bayes, and its in-depth implementation, classification performance evaluation, model selection and tuning, and cross-validation. Examples such as spam e-mail detection are demonstrated.
, News Topic Classification with Support Vector Machine, covers multiclass classification, Support Vector Machine, and how it is applied in topic classification. Other important concepts, such as kernel machine, overfitting, and regularization, are discussed as well.
, Click-Through Prediction with Tree-Based Algorithms
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