Python Machine Learning By Example, Third Edition
Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques
What Amazon Readers Say:
What You Will Learn
A comprehensive gateway into the world of ML
Understand the important concepts in ML 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 analysis 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 2, PyTorch, and scikit-learn
About the Author
Yuxi (Hayden) Liu is a machine learning software engineer at Google. Previously he worked as a machine learning scientist in a variety of data-driven domains and applied his machine learning expertise in computational advertising, and cybersecurity. Hayden is the author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages.
Full Chapter Overview
Getting Started with Machine Learning and Python
What is ML? Why do we need it? You will explore typical ML tasks and essential techniques of working with data and models.
Building a Movie Recommendation Engine with Naive Bayes
You will learn the fundamental concepts of classification, and build a movie recommender using Naive Bayes. You will practice fine-tuning a model - an important skill for every ML practitioner.
Recognizing Faces with Support Vector Machine
Support vector machine searches for a decision boundary to separate data from different classes. You will implement the algorithm and apply it to face recognition.
Predicting Online Ad Click-Through with Tree-Based Algorithms
Tree-based models (decision trees, random forests, boosted trees) throughout the course of solving the ad click-through rate problem. Feature importance, and model ensemble will also be covered.
Predicting Online Ad Click-Through with Logistic Regression
Logistic regression is a very scalable classification model. You will use it on large datasets, learn about categorical encoding, regularization, online learning, and stochastic gradient descent.
Scaling Up Prediction to Terabyte Click Logs
Parallel computing tools Apache Hadoop and Spark - a more scalable solution to massive ad click prediction.
Predicting Stock Prices with Regression Algorithms
Several popular regression algorithms, including linear regression, regression tree, and support vector regression for you to tackle a billion (or trillion) dollar problem - stock price prediction.
Predicting Stock Prices with Artificial Neural Networks
Neural network models in depth. You will start by building the simplest neural network and go deeper by adding more layers, and implement it using TensorFlow to predict stock prices.
Mining the 20 Newsgroups Dataset with Text Analysis Techniques
You will gain hands-on experience in neural language processing through working with text data, and visualize text data using dimension reduction techniques.
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Using K-means algorithm, matrix factorization and latent Dirichlet allocation, you will be amused by how many interesting themes are mined from the 20 newsgroups dataset!
Machine Learning Best Practices
Get ready for real-world projects and follow 21 best practices throughout the entire machine learning workflow.
Categorizing Images of Clothing with Convolutional Neural Networks
Building blocks and architecture of CNNs, and their implementation in TensorFlow. You will categorize clothing images, and utilize data augmentation techniques to boost the CNN classifier.
Making Predictions with Sequences using Recurrent Neural Networks
Sequential learning, and how RNNs are well suited for it. You will solve two sequential learning problems: movie review sentiment analysis and text auto-generation.
Making Decisions in Complex Environments with Reinforcement Learning
Learning from experience, and interacting with the environment. You will explore various RL environments with dynamic programming algorithms, Monte Carlo learning, and Q-learning.
Who Should Read This Book?
Machine Learning Enthusiast
- Data analyst and data scientist
- Data engineer, machine learning engineer, software engineer
- Who's highly passionate about machine learning
To get the most out of this book
You are expected to have a basic foundation of knowledge of Python, and some basic Python libraries, such as Numpy
Endorsements from Guru
Python Machine Learning by Example, Third Edition is ideal for those who learn best by doing. I think for the ML beginner, this book may be a better starting point than one with much more about theory and less focus on the practical aspects of ML."
Alex Martelli, Fellow, Python Software Foundation, Co-author of Python Cookbook and Python in a Nutshell