Book description
Grasp machine learning concepts, techniques, and algorithms with the help of realworld examples using Python libraries such as TensorFlow and scikitlearn
Key Features
 Exploit the power of Python to explore the world of data mining and data analytics
 Discover machine learning algorithms to solve complex challenges faced by data scientists today
 Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects
Book Description
The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you're interested in ML, this book will serve as your entry point to ML.
Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You'll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easytofollow way.
With the help of this extended and updated edition, you'll understand how to tackle datadriven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikitlearn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easytofollow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.
By the end of the book, you'll have put together a broad picture of the ML ecosystem and will be wellversed with the best practices of applying ML techniques to make the most out of new opportunities.
What you will learn
 Understand the important concepts in machine learning and data science
 Use Python to explore the world of data mining and analytics
 Scale up model training using varied data complexities with Apache Spark
 Delve deep into text and NLP using Python libraries such NLTK and gensim
 Select and build an ML model and evaluate and optimize its performance
 Implement ML algorithms from scratch in Python, TensorFlow, and scikitlearn
Who this book is for
If you're a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Dedication
 Foreword
 Contributors
 Preface
 Section 1: Fundamentals of Machine Learning
 Getting Started with Machine Learning and Python
 Section 2: Practical Python Machine Learning By Example
 Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
 Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
 Detecting Spam Email with Naive Bayes

Classifying Newsgroup Topics with Support Vector Machines
 Finding separating boundary with support vector machines
 Classifying newsgroup topics with SVMs
 More example – fetal state classification on cardiotocography
 A further example – breast cancer classification using SVM with TensorFlow
 Summary
 Exercise

Predicting Online Ad ClickThrough with TreeBased Algorithms
 Brief overview of advertising clickthrough prediction
 Getting started with two types of data – numerical and categorical
 Exploring decision tree from root to leaves
 Implementing a decision tree from scratch
 Predicting ad clickthrough with decision tree
 Ensembling decision trees – random forest
 Summary
 Exercise

Predicting Online Ad ClickThrough with Logistic Regression
 Converting categorical features to numerical – onehot encoding and ordinal encoding
 Classifying data with logistic regression
 Training a logistic regression model
 Training on large datasets with online learning
 Handling multiclass classification
 Implementing logistic regression using TensorFlow
 Feature selection using random forest
 Summary
 Exercises
 Scaling Up Prediction to Terabyte Click Logs

Stock Price Prediction with Regression Algorithms
 Brief overview of the stock market and stock prices
 What is regression?
 Mining stock price data
 Estimating with linear regression
 Estimating with decision tree regression
 Estimating with support vector regression
 Estimating with neural networks
 Evaluating regression performance
 Predicting stock price with four regression algorithms
 Summary
 Exercise
 Section 3: Python Machine Learning Best Practices

Machine Learning Best Practices
 Machine learning solution workflow
 Best practices in the data preparation stage

Best practices in the training sets generation stage
 Best practice 6 – identifying categorical features with numerical values
 Best practice 7 – deciding on whether or not to encode categorical features
 Best practice 8 – deciding on whether or not to select features, and if so, how to do so
 Best practice 9 – deciding on whether or not to reduce dimensionality, and if so, how to do so
 Best practice 10 – deciding on whether or not to rescale features
 Best practice 11 – performing feature engineering with domain expertise
 Best practice 12 – performing feature engineering without domain expertise
 Best practice 13 – documenting how each feature is generated
 Best practice 14 – extracting features from text data
 Best practices in the model training, evaluation, and selection stage
 Best practices in the deployment and monitoring stage
 Summary
 Exercises
 Other Books You May Enjoy
Product information
 Title: Python Machine Learning By Example  Second Edition
 Author(s):
 Release date: February 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789616729
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