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)

Measuring t-statistics and p-values using Python

Let's fabricate some experimental data and use the t-statistic and p-value to determine whether a given experimental result is a real effect or not. We're going to actually fabricate some fake experimental data and run t-statistics and p-values on them, and see how it works and how to compute it in Python.

Running A/B test on some experimental data

Let's imagine that we're running an A/B test on a website and we have randomly assigned our users into two groups, group A and group B. The A group is going to be our test subjects, our treatment group, and group B will be our control, basically the way the website used to be. We'll set this up with the following...