Book Image

Python Machine Learning Blueprints - Second Edition

By : Alexander Combs, Michael Roman
Book Image

Python Machine Learning Blueprints - Second Edition

By: Alexander Combs, 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)

Content-based filtering

As a musician himself, Tim Westergren had spent years on the road listening to other talented musicians, wondering why they could never get ahead. Their music was good—just as good as anything you might hear on the radio—and yet, somehow, they just never caught their big break. He imagined it must be because their music just never got in front of enough of the right people.

Tim eventually quit his job as a musician and took another job as a composer for movie scores. It was there that he began to think of each piece of music as having a distinct structure that could be decomposed into constituent partsa form of musical DNA.

After giving it some thought, he began to consider creating a company around this idea of building a musical genome. He ran the concept by one of his friends, who had previously created and sold a company. The friend...