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)

Bayesian methods - Concepts

Did you ever wonder how the spam classifier in your e-mail works? How does it know that an e-mail might be spam or not? Well, one popular technique is something called Naive Bayes, and that's an example of a Bayesian method. Let's learn more about how that works. Let's discuss Bayesian methods.

We did talk about Bayes' theorem earlier in this book in the context of talking about how things like drug tests could be very misleading in their results. But you can actually apply the same Bayes' theorem to larger problems, like spam classifiers. So let's dive into how that might work, it's called a Bayesian method.

So just a refresher on Bayes' theorem -remember, the probability of A given B is equal to the overall probability of A times the probability of B given A over the overall probability of B:

How can we use...