Book Image

Hands-On Data Science and Python Machine Learning

By : Frank Kane
Book Image

Hands-On Data Science and Python Machine Learning

By: Frank Kane

Overview of this book

Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
Table of Contents (11 chapters)

Introducing MLlib

Fortunately, you don't have to do things the hard way in Spark when you're doing machine learning. It has a built-in component called MLlib that lives on top of Spark Core, and this makes it very easy to perform complex machine learning algorithms using massive Datasets, and distributing that processing across an entire cluster of computers. So, very exciting stuff. Let's learn more about what it can do.

Some MLlib Capabilities

So, what are some of the things MLlib can do? Well, one is feature extraction.

One thing you can do at scale is term frequency and inverse document frequency stuff, and that's useful for creating, for example, search indexes. We will actually go through an example...