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)

Covariance and correlation

Next, we're going to talk about covariance and correlation. Let's say I have two different attributes of something and I want to see if they're actually related to each other or not. This section will give you the mathematical tools you need to do so, and we'll dive into some examples and actually figure out covariance and correlation using Python. These are ways of measuring whether two different attributes are related to each other in a set of data, which can be a very useful thing to find out.

Defining the concepts

Imagine we have a scatter plot, and each one of the data points represents a person that we measured, and we're plotting their age on one axis versus their...