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)

Standard deviation and variance

Let's talk about standard deviation and variance. The concepts and terms you've probably heard before, but let's go into a little bit more depth about what they really mean and how you compute them. It's a measure of the spread of a data distribution, and that will make a little bit more sense in a few minutes.

Standard deviation and variance are two fundamental quantities for a data distribution that you'll see over and over again in this book. So, let's see what they are, if you need a refresher.

Variance

Let's look at a histogram, because variance and standard deviation are all about the spread of the data, the shape of the distribution of a dataset. Take...