Python Machine Learning Blueprints - Second Edition

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras

Python Machine Learning Blueprints - Second Edition

Alexander Combs, Michael Roman
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Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras
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Book Details

ISBN 139781788994170
Paperback378 pages

Book Description

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.

The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.

By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.

Table of Contents

Chapter 11: What's Next?

What You Will Learn

  • Understand the Python data science stack and commonly used algorithms
  • Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window 
  • Understand NLP concepts by creating a custom news feed
  • Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked
  • Gain the skills to build a chatbot from scratch using PySpark
  • Develop a market-prediction app using stock data
  • Delve into advanced concepts such as computer vision, neural networks, and deep learning

Authors

Table of Contents

Chapter 11: What's Next?

Book Details

ISBN 139781788994170
Paperback378 pages
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