Hands-On Data Science and Python Machine Learning

Key Features

• Take your first steps in the world of data science by understanding the tools and techniques of data analysis
• Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods
• Learn how to use Apache Spark for processing Big Data efficiently

Book Description

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.

What you will learn

• Learn how to clean your data and ready it for analysis
• Implement the popular clustering and regression methods in Python
• Train efficient machine learning models using decision trees and random forests
• Visualize the results of your analysis using Python’s Matplotlib library
• Use Apache Spark’s MLlib package to perform machine learning on large datasets

About the Author

My name is Frank Kane. I spent nine years at Amazon and IMDb, wrangling millions of customer ratings and customer transactions to produce things such as personalized recommendations for movies and products and “people who bought this also bought.” I tell you, I wish we had Apache Spark back then, when I spent years trying to solve these problems there. I hold 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, I left to start my own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Table of Contents

Chapter 1. Getting Started
Chapter 2. Statistics and Probability Refresher and Python Practice
Chapter 3. Matplotlib and Advanced Probability Concepts
Chapter 4. Predictive Models
Chapter 5. Machine Learning with Python
Chapter 6. Recommender Systems
Chapter 7. More Data Mining and Machine Learning Techniques
Chapter 8. Dealing with Real-World Data
Chapter 9. Apache Spark: Machine Learning on Big Data
Chapter 10. Testing and Experimental Design