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)

K-nearest neighbors - concepts

Let's talk about a few data mining and machine learning techniques that employers expect you to know about. We'll start with a really simple one called KNN for short. You're going to be surprised at just how simple a good supervised machine learning technique can be. Let's take a look!

KNN sounds fancy but it's actually one of the simplest techniques out there! Let's say you have a scatter plot and you can compute the distance between any two points on that scatter plot. Let's assume that you have a bunch of data that you've already classified, that you can train the system from. If I have a new data point, all I do is look at the KNN based on that distance metric and let them all vote on the classification of that new point.

Let's imagine that the following scatter plot is plotting movies. The squares...