Python Machine Learning

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Key Features

• Third edition of the bestselling, widely acclaimed Python machine learning book
• Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
• Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

• Master the frameworks, models, and techniques that enable machines to 'learn' from data
• Use scikit-learn for machine learning and TensorFlow for deep learning
• Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more
• Build and train neural networks, GANs, and other models
• Discover best practices for evaluating and tuning models
• Predict continuous target outcomes using regression analysis
• Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Table of Contents

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using scikit-learn
4. Building Good Training Datasets – Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Embedding a Machine Learning Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data – Clustering Analysis
12. Implementing a Multilayer Artificial Neural Network from Scratch
13. Parallelizing Neural Network Training with TensorFlow

Machine learning

Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning from data, without being explicitly programmed. In other words, instead of providing explicit instructions, machine learning algorithms use data to learn patterns and make predictions or decisions. 

Here are several definitions of machine learning:

1. Arthur Samuel's Classic Definition: "Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed." This definition is one of the earliest and most well-known descriptions of machine learning.
2. Tom Mitchell's Practical Definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition emphasizes that machine learning is about improving performance on specific tasks through experience.
3. Wikipedia's Definition: "Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to 'learn' with data, without being explicitly programmed." This definition highlights the statistical nature of machine learning.
4. Arthur Samuel's Expanded Definition: "Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases." This definition emphasizes the role of algorithms and empirical data.
5. Microsoft's Definition: "Machine learning is a data analysis technique that teaches computers to do what comes naturally to humans and animals: learn from experience." This definition connects machine learning to the natural learning process.
6. Google's Definition: "Machine learning is the study of algorithms and statistical models that computer systems use to perform a task without using explicit instructions, relying on patterns and inference instead." This definition highlights the reliance on patterns and inference in machine learning.

There are three main types of machine learning:

1. Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each input (or feature) is associated with the correct output (or label). The goal is to learn a mapping from inputs to outputs, allowing the model to make predictions on new, unseen data. Common supervised learning algorithms include linear regression, decision trees, and neural networks.
2. Unsupervised Learning: Unsupervised learning involves training models on unlabeled data. The goal is to discover patterns, structures, or relationships within the data. Clustering and dimensionality reduction are common tasks in unsupervised learning. K-means clustering and principal component analysis (PCA) are examples of unsupervised learning algorithms.
3. Reinforcement Learning: Reinforcement learning is about training agents to make sequences of decisions in an environment to maximize a cumulative reward. The agent learns by interacting with its environment and receiving feedback in the form of rewards or punishments. This type of learning is commonly used in areas like robotics, game playing, and autonomous systems.

Machine learning is applied to a wide range of applications, including:

1. Natural Language Processing (NLP): Sentiment analysis, language translation, chatbots, and text generation.
2. Computer Vision: Image recognition, object detection, and facial recognition.
3. Recommendation Systems: Product recommendations, content suggestions, and personalized marketing.
4. Healthcare: Disease diagnosis, drug discovery, and patient outcome prediction.
5. Finance: Credit scoring, fraud detection, and stock price forecasting.
6. Autonomous Vehicles: Self-driving cars and drones.
7. Industrial Processes: Predictive maintenance and quality control.

To implement machine learning, you typically follow a process that includes data collection and preprocessing, model selection and training, evaluation, and deployment. The field of machine learning continues to evolve with ongoing research and development, and it plays a crucial role in many technological advancements.

Here are some recommended books for learning machine learning, suitable for different levels of expertise:

1. "Introduction to Machine Learning with Python" by Andreas C. Müller & Sarah Guido: This book provides a practical introduction to machine learning with Python, focusing on scikit-learn and other popular libraries.
2. "Pattern Recognition and Machine Learning" by Christopher M. Bishop: This is a comprehensive textbook that covers the fundamentals of pattern recognition and machine learning. It's a great resource for those looking to dive deep into the mathematical aspects of machine learning.
3. "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy: This book emphasizes a probabilistic approach to machine learning, making it suitable for those with a background in statistics and mathematics.
4. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: For those interested in deep learning, this is a definitive textbook that covers the foundations and techniques used in deep neural networks.
5. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: This practical book takes you through the hands-on implementation of various machine learning and deep learning models using popular libraries.
6. "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili: A beginner-friendly book that introduces machine learning concepts and their implementation in Python. It covers a wide range of topics and is suitable for newcomers to the field.
7. "Machine Learning Yearning" by Andrew Ng: Written by one of the pioneers in the field, this book is more of a guide to developing machine learning projects and strategies. It focuses on best practices and how to approach machine learning problems.
8. "The Hundred-Page Machine Learning Book" by Andriy Burkov: This is a concise and practical guide that covers the essentials of machine learning in a relatively short book.
9. "Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani: If you're specifically interested in computer vision and deep learning, this book is a great resource that covers various techniques and applications

Remember that the choice of the book depends on your current knowledge and what specific aspects of machine learning you're interested in. It's a good idea to start with an introductory book if you're new to the field and then progress to more advanced texts as you gain expertise.