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 started our journey with building the most important stepping stone of the book - Installing Enthought Canopy. We then moved to installing other libraries and installing different types of packages. We also grasped some of the basics of Python with the help of various Python code. We covered basic concepts such as modules, lists along with Tuples, and eventually moved on to understanding more of Python basics with a better knowledge of functions and looping in Python. Finally, we started with running some of our simple Python scripts.

In the next chapter, we will move on to understand concepts of statistics and probability.