Predictive Modeling and Machine Learning with MATLAB (Coursera)

To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization.

By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.

Course 3 of 4 in the Practical Data Science with MATLAB Specialization.

Syllabus

WEEK 1

Creating Regression Models

In this module you'll apply the skills gained from the first two courses in the specialization on a new dataset. You'll be introduced to the Supervised Machine Learning Workflow and learn key terms. You'll end the module by creating and evaluating regression machine learning models.

WEEK 2

Creating Classification Models

In this module you'll learn the basics of classification models. You'll train several types of classification models and evaluation the results.

WEEK 3

Applying the Supervised Machine Learning Workflow

In this module you'll apply the complete supervised machine learning workflow. You'll use validation data inform model creation. You'll apply different feature selection techniques to reduce model complexity. You'll create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project.

WEEK 4

Advanced Topics and Next Steps