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)

Getting Started

Since there's going to be code associated with this book and sample data that you need to get as well, let me first show you where to get that and then we'll be good to go. We need to get some setup out of the way first. First things first, let's get the code and the data that you need for this book so you can play along and actually have some code to mess around with. The easiest way to do that is by going right to this - Getting Started.

In this chapter, we will first install and get ready in a working Python environment:

  • Installing Enthought Canopy
  • Installing Python libraries
  • How to work with the IPython/Jupyter Notebook
  • How to use, read and run the code files for this book
  • Then we'll dive into a crash course into understanding Python code:
  • Python basics - part 1
  • Understanding Python code
  • Importing modules
  • Experimenting with lists
  • Tuples
  • Python basics - part 2
  • Running Python scripts

You'll have everything you need for an amazing journey into data science with Python, once we've set up your environment and familiarized you with Python in this chapter.