Python Machine Learning By Example - Second Edition: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition - Softcover

Reseña del editor

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn

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 easy-to-follow way.

With the help of this extended and updated edition, you'll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow 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 well-versed 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 scikit-learn

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.

Table of Contents

1. Getting Started with Machine Learning and Python
2. Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
3. Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
4. Detecting Spam Email with Naive Bayes
5. Classifying News Topic with Support Vector Machine
6. Predicting Online Ads Click-through with Tree-Based Algorithms
7. Predicting Online Ads Click-through with Logistic Regression
8. Scaling Up Prediction to Terabyte Click Logs
9. Stock Price Prediction with Regression Algorithms
10. Machine Learning Best Practices

Biografía del autor

Yuxi (Hayden) Liu is an 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 a #1 bestseller in Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. He is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.