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    Python Machine Learning By Example

    Posted By: AlenMiler
    Python Machine Learning By Example

    Python Machine Learning By Example by Yuxi (Hayden) Liu
    English | 31 May 2017 | ASIN: B01MT7ATL5 | 254 Pages | AZW3 | 3.86 MB

    Key Features

    Learn the fundamentals of machine learning and build your own intelligent applications
    Master the art of building your own machine learning systems with this example-based practical guide
    Work with important classification and regression algorithms and other machine learning techniques

    Book Description

    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.

    This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.

    Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

    What you will learn

    Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
    Use Python to visualize data spread across multiple dimensions and extract useful features
    Dive deep into the world of analytics to predict situations correctly
    Implement machine learning classification and regression algorithms from scratch in Python
    Be amazed to see the algorithms in action
    Evaluate the performance of a machine learning model and optimize it
    Solve interesting real-world problems using machine learning and Python as the journey unfolds

    About the Author

    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.

    Table of Contents

    Getting Started with Python and Machine Learning
    Exploring the 20 newsgroups data set
    Spam email detection with Naive Bayes
    News topic classification with Support Vector Machine
    Click-through prediction with tree-based algorithms
    Click-through rate prediction with logistic regression
    Stock prices prediction with regression algorithms
    Best practices