Python Machine Learning Blueprints - Second Edition

More Information
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
About

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.

Features
  • Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras
  • Implement advanced concepts and popular machine learning algorithms in real-world projects
  • Build analytics, computer vision, and neural network projects
Page Count 378
Course Length 11 hours 20 minutes
ISBN 9781788994170
Date Of Publication 31 Jan 2019

Authors

Alexander Combs

Alexander Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He currently lives and works in New York City.

Michael Roman

Michael Roman is a data scientist at The Atlantic, where he designs, tests, analyzes, and productionizes machine learning models to address a range of business topics. Prior to this he was an associate instructor at a full-time data science immersive program in New York City. His interests include computer vision, propensity modeling, natural language processing, and entrepreneurship.