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)

Summary

In this chapter, we talked about regression analysis, which is trying to fit a curve to a set of training data and then using it to predict new values. We saw its different forms. We looked at the concept of linear regression and its implementation in Python.

We learned what polynomial regression is, that is, using higher degree polynomials to create better, complex curves for multi-dimensional data. We also saw its implementation in Python.

We then talked about multivariate regression, which is a little bit more complicated. We saw how it is used when there are multiple factors affecting the data that we're predicting. We looked at an interesting example, which predicts the price of a car using Python and a very powerful tool, pandas.

Finally, we looked at the concept of multi-level models. We understood some of the challenges and how to think about them when you...