Python: Real World Machine Learning

Summary

In this introductory chapter, we have illustrated the different ways in which we can make machine learning algorithms scalable using Python (scale up and scale out techniques). We also proposed some motivating examples and set the stage for the book by illustrating how to install Python on your machine. In particular, we introduced you to Jupyter and covered all the most important packages that will be used in this book.

In the next chapter, we will dive into discussing how stochastic gradient descent can help you deal with massive datasets by leveraging I/O on a single machine. Basically, we will cover different ways of streaming data from large files or data repositories and feed it into a basic learning algorithm. You will be amazed ...