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


Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition
By 作者: Yuxi (Hayden) Liu
ISBN-10 书号: 1789616727
ISBN-13 书号: 9781789616729
Release Finelybook 出版日期: 2019-02-28
pages 页数: (382 )

The Book Description

More Information
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
About
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.

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

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 Newsgroup Topics with Support Vector Machines
6 Predicting Online Ad Click-Through with Tree-Based Algorithms
7 Predicting Online Ad 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

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