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)

Sourcing apartment listing data

In the early 1970s, if you wanted to purchase a stock, you would need to engage a broker, who would charge you a fixed commission of nearly 1%. If you wanted to purchase an airline ticket, you would need to contact a travel agent, who would earn a commission of around 7%. And if you wanted to sell a home, you would contact a real estate agent, who would earn a commission of 6%. In 2018, you can do the first two essentially for free. The last one remains as it was in the 1970s.

Why is this the case and, more importantly, what does any of this have to do with machine learning? The reality is, it all comes down to data, and who has access to that data.

You might assume that you could easily access troves of real estate listing data quite easily through APIs or by web scraping real estate websites. You would be wrong. Well, wrong if you intend to follow...