This course will take a practical approach to learning machine learning. We'll cover the basics of why things are happening (like why and how the algorithms are able to adapt to (learn from) the data), but we'll spend the majority of the time applying time-tested machine learning libraries and methods to real data. We'll then analyze the results and demonstrate how to calculate the accuracy of the methods applied.
This course is intended for those people interested in learning how to deploy time tested machine learning methodologies on data in their organizations. It is a hands on course requiring a development environment setup for Python coding. The course assumes no prior knowledge in machine learning, but it does require some familiarity with Python programming.
Linear Regression
Logistic regression
Trees
Unsupervised learning
Brief intro to Neural Networks