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Python Machine Learning Blueprints

Python Machine Learning Blueprints - Second Edition

By : Alexander Combs, Saurabh Chhajed, Michael Roman
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Python Machine Learning Blueprints

Python Machine Learning Blueprints

By: Alexander Combs, Saurabh Chhajed, Michael 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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Summary

In this chapter, we've looked at the inner workings of the stock market and explored a number of ways to approach utilizing machine learning in a trading strategy. There is no doubt the material in this chapter could fill a book itself. We even failed to cover some of the most important aspects of trading, such as portfolio construction, risk mitigation, and money management. These are critical components to any strategy, perhaps even more important than trade signals.

Hopefully, this will serve as a jumping-off point for your own explorations, but again, I caution you that beating the market is a nearly impossible game—one in which you are competing against the brightest minds in the world. If you do decide to try, I wish you the best of luck. Just remember that I warned you if it doesn't turn out like you hoped!

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Python Machine Learning Blueprints
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