In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background.
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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
