Regression Training and Testing 
Regression Training and Testing
by Harrison Kinsley
Video Lecture 4 of 42
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Date Added: August 11, 2016

Lecture Description

Welcome to part four of the Machine Learning with Python tutorial series. In the previous tutorials, we got our initial data, we transformed and manipulated it a bit to our liking, and then we began to define our features. Scikit-Learn does not fundamentally need to work with Pandas and dataframes, I just prefer to do my data-handling with it, as it is fast and efficient. Instead, Scikit-learn actually fundamentally requires numpy arrays. Pandas dataframes can be easily converted to NumPy arrays, so it just so happens to work out for us!

It is a typical standard with machine learning in code to define X (capital x), as the features, and y (lowercase y) as the label that corresponds to the features. As such, we can define our features and labels like so.

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Course Index

Course Description

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

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