Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas

Key Features:

- Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling

- Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions

- Implement ML models, such as neural networks and linear and logistic regression, from scratch

- Purchase of the print or Kindle book includes a free PDF copy

Book Description:

The fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by experienced machine learning author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts.

Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You'll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.

This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

What You Will Learn:

- Follow machine learning best practices across data preparation and model development

- Build and improve image classifiers using Convolutional Neural Networks (CNNs) and transfer learning

- Develop and fine-tune neural networks using TensorFlow and PyTorch

- Analyze sequence data and make predictions using RNNs, transformers, and CLIP

- Build classifiers using SVMs and boost performance with PCA

- Avoid overfitting using regularization, feature selection, and more

Who this book is for:

This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

Table of Contents

- Getting Started with Machine Learning and Python

- Building a Movie Recommendation Engine

- Predicting Online Ad Click-Through with Tree-Based Algorithms

- Predicting Online Ad Click-Through with Logistic Regression

- Predicting Stock Prices with Regression Algorithms

- Predicting Stock Prices with Artificial Neural Networks

- Mining the 20 Newsgroups Dataset with Text Analysis Techniques

- Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

- Recognizing Faces with Support Vector Machine

- Machine Learning Best Practices

- Categorizing Images of Clothing with Convolutional Neural Networks

- Making Predictions with Sequences Using Recurrent Neural Networks

- Advancing Language Understanding and Generation with Transformer Models

- Building An Image Search Engine Using Multimodal Models

- Making Decisions in Complex Environments with Reinforcement Learning

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

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

Book Description

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 Naïve 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.

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

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.

Take tiny steps to enter the big world of data science through this interesting guide

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

Who this book is for:

This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed.

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text ...