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)

Ensemble learning

When we talked about random forests, that was an example of ensemble learning, where we're actually combining multiple models together to come up with a better result than any single model could come up with. So, let's learn about that in a little bit more depth. Let's talk about ensemble learning a little bit more.

So, remember random forests? We had a bunch of decision trees that were using different subsamples of the input data, and different sets of attributes that it would branch on, and they all voted on the final result when you were trying to classify something at the end. That's an example of ensemble learning. Another example: when we were talking about k-means clustering, we had the idea of maybe using different k-means models with different initial random centroids, and letting them all vote on the final result as well. That is...