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)

Searching wikipedia with Spark MLlib

We're going to build an actual working search algorithm for a piece of Wikipedia using Apache Spark in MLlib, and we're going to do it all in less than 50 lines of code. This might be the coolest thing we do in this entire book!

Go into your course materials and open up the TF-IDF.py script, and that should open up Canopy with the following code:

Now, step back for a moment and let it sink in that we're actually creating a working search algorithm, along with a few examples of using it in less than 50 lines of code here, and it's scalable. I could run this on a cluster. It's kind of amazing. Let's step through the code.

Import statements

We're going to...