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)

Linear regression

Let's talk about regression analysis, a very popular topic in data science and statistics. It's all about trying to fit a curve or some sort of function, to a set of observations and then using that function to predict new values that you haven't seen yet. That's all there is to linear regression!

So, linear regression is fitting a straight line to a set of observations. For example, let's say that I have a bunch of people that I measured and the two features that I measured of these people are their weight and their height:

I'm showing the weight on the x-axis and the height on the y-axis, and I can plot all these data points, as in the people's weight versus their height, and I can say, "Hmm, that looks like a linear relationship, doesn't it? Maybe I can fit a straight line to it and use that to predict new values...