Python Machine Learning By Example Third Edition

Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Yuxi (Hayden) Liu 지음

Packt(GCO Science) 2020년 10월 30일 출간

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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 You Will Learn
？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

▶Key Features
？Dive into machine learning algorithms to solve the complex challenges faced by data scientists today
？Explore cutting edge content reflecting deep learning and reinforcement learning developments
？Use updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end

▶Who This Book Is For
If you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.

Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.

목차

▶TABLE of CONTENTS
？Chapter 1: Getting Started with Machine Learning and Python
？Chapter 2: Building a Movie Recommendation Engine with Na？ve Bayes
？Chapter 3: Recognizing Faces with Support Vector Machine
？Chapter 4: Predicting Online Ad Click-Through with Tree-Based Algorithms
？Chapter 5: Predicting Online Ad Click-Through with Logistic Regression
？Chapter 6: Scaling Up Prediction to Terabyte Click Logs
？Chapter 7: Predicting Stock Prices with Regression Algorithms
？Chapter 8: Predicting Stock Prices with Artificial Neural Networks
？Chapter 9: Mining the 20 Newsgroups Dataset with Text Analysis Techniques
？Chapter 10: Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
？Chapter 11: Machine Learning Best Practices
？Chapter 12: Categorizing Images of Clothing with Convolutional Neural Networks
？Chapter 13: Making Predictions with Sequences Using Recurrent Neural Networks
？Chapter 14: Making Decisions in Complex Environments with Reinforcement Learning

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▶What this book covers
？ Chapter 1, Getting Started with Machine Learning and Python, will kick off your Python machine learning journey. It will start with what machine learning is, why we need it, and its evolution over the last few decades. It will then discuss typical machine learning tasks and explore several essential techniques of working with data and working with models, in a practical and fun way. You will also set up the software and tools needed for examples and projects in the upcoming chapters.

？ Chapter 2, Building a Movie Recommendation Engine with Na？ve Bayes, will focus on classification, specifically binary classification and Na？ve Bayes. The goal of the chapter is to build a movie recommendation system. You will learn the fundamental concepts of classification, and about Na？ve Bayes, a simple yet powerful algorithm. It will also demonstrate how to fine-tune a model, which is an important skill for every data science or machine learning practitioner to learn.

？ Chapter 3, Recognizing Faces with Support Vector Machine, will continue the journey of supervised learning and classification. Specifically, it will focus on multiclass classification and support vector machine classifiers. It will discuss how the support vector machine algorithm searches for a decision boundary in order to separate data from different classes. Also, you will implement the algorithm with scikit-learn, and apply it to solve various real-life problems including face recognition.

？ Chapter 4, Predicting Online Ad Click-Through with Tree-Based Algorithms, will introduce and explain in depth tree-based algorithms (including decision trees, random forests, and boosted trees) throughout the course of solving the advertising click-through rate problem. You will explore decision trees from the root to the leaves, and work on implementations of tree models from scratch, using scikit-learn and XGBoost. Feature importance, feature selection, and ensemble will be covered alongside.

？ Chapter 5, Predicting Online Ad Click-Through with Logistic Regression, will be a continuation of the ad click-through prediction project, with a focus on a very scalable classification model―logistic regression. You will explore how logistic regression works, and how to work with large datasets. The chapter will also cover categorical variable encoding, L1 and L2 regularization, feature selection, online learning, and stochastic gradient descent.

？ Chapter 6, Scaling Up Prediction to Terabyte Click Logs, will be about a more scalable solution to massive ad click prediction, utilizing powerful parallel computing tools including Apache Hadoop and Spark. It will cover the essential concepts of Spark such as installation, RDD, and core programming, as well its ML components. You will work with the entire ad click dataset, build classification models, and perform feature engineering and performance evaluation using Spark.

？ Chapter 7, Predicting Stock Prices with Regression Algorithms, will focus on several popular regression algorithms, including linear regression, regression tree and regression forest, and support vector regression. It will encourage you to utilize them to tackle a billion (or trillion) dollar problem―stock price prediction. You will practice solving regression problems using scikit-learn and TensorFlow.

？ Chapter 8, Predicting Stock Prices with Artificial Neural Networks, will introduce and explain in depth neural network models. It will cover the building blocks of neural networks, and important concepts such as activation functions, feedforward, and backpropagation. You will start by building the simplest neural network and go deeper by adding more layers to it. We will implement neural networks from scratch, use TensorFlow and Keras, and train a neural network to predict stock prices.
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▶ Preface
Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).

With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.

At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.

Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.

By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

작가정보

저자(글) Yuxi (Hayden) Liu

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, marketing, 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. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and PyTorch 1.x Reinforcement Learning Cookbook.