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Python Machine Learning Blueprints

Python Machine Learning Blueprints - Second Edition

By : Combs, Chhajed, Roman
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Python Machine Learning Blueprints

Python Machine Learning Blueprints

By: Combs, Chhajed, Roman

Overview of this book

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 (13 chapters)
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What does research tell us about the stock market?

Perhaps the most influential theory of the stock market over the last 50 years is that of the efficient market hypothesis. This theory, developed by Eugene Fama, stipulates that markets are rational and that all the available information is appropriately reflected in stock prices. As such, it is impossible for an investor to consistently beat the market on a risk-adjusted basis. The efficient market hypothesis is often discussed as having three forms: a weak form, a semi-strong form, and a strong form:

  1. In the weak form, the market is efficient in the sense that you cannot use past information from prices to predict future prices. Information is reflected in stocks relatively quickly, and while technical analysis would be ineffective, in some scenarios, fundamental analysis could be effective.
  1. In the semi-strong form, prices...
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Python Machine Learning Blueprints
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