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)

Classifying Images with Convolutional Neural Networks

In this chapter, we're going to explore the vast and awesome world of computer vision.

If you've ever wanted to construct a predictive machine learning model using image data, this chapter will serve as an easily-digestible and practical resource. We'll go step by step through building an image-classification model, cross-validating it, and then building it in a better way. At the end of this chapter, we'll have a darn good model and discuss some paths for future enhancement.

Of course, some background in the fundamentals of predictive modeling will help this to go smoothly. As you'll soon see, the process of converting images into usable features for our model might might feel new, but once our features are extracted, the model-building and cross-validation processes are exactly the same.

In this chapter...