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)

Dimensionality reduction and principal component analysis

Alright, time to get all trippy! We're going to talking about higher dimensions, and dimensionality reduction. Sounds scary! There is some fancy math involved, but conceptually it's not as hard to grasp as you might think. So, let's talk about dimensionality reduction and principal component analysis next. Very dramatic sounding! Usually when people talk about this, they're talking about a technique called principal component analysis or PCA, and a specific technique called singular value decomposition or SVD. So PCA and SVD are the topics of this section. Let's dive into it!

Dimensionality reduction

So, what is the curse of dimensionality?...