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)

Building a chatbot

Now, having seen what's possible in terms of chatbots, you most likely want to build the best, most state-of-the-art, Google-level bot out there, right? Well, just put that out of your mind for now because we're going start by doing the exact opposite. We're going to build the most amazingly awful bot ever!

This may sound disappointing, but if your goal is just to build something very cool and engaging (that doesn't take hours and hours to construct), this is a great place to start.

We're going to leverage the training data derived from a set of real conversations with Cleverbot. The data was collected from This site is perfect, as it has people submit the most absurd conversations they had with Cleverbot.

Let's take a look at a sample conversation between Cleverbot and a user from the site: