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Be introduced to the most common Machine Learning algorithms used in supervised and unsupervised learning models.


Understanding the complexities of model construction, training, and testing is an essential skill for today’s Machine Learning Engineer. High school students completing this second course in the Machine Learning certificate program will gain a working knowledge of the most common models used in both supervised and unsupervised learning algorithms, including Regression, Naive Bayes, K-nearest neighbors, K-means, and DBSCAN. High school students will also utilize dimension reduction techniques such as Principal Component Analysis, and Linear Discriminant Analysis to pre-process datasets prior to model training.

What You Will Learn

  • Create Python programming language scripts in the Google Collaboratory development environment to pre-process a dataset using standard Machine Learning libraries.
  • Implement and analyze regression models including simple and multiple regression, polynomial, lasso, and logistic regression.
  • Implement and analyze “supervised” classification algorithms including Naive Bayes and  K nearest neighbors (KNN).
  • Implement and analyze “unsupervised” clustering algorithms including K-means, and density-based spatial (DBSCAN) clustering.
  • Implement and analyze dimensionality reduction techniques including linear discriminant analysis (LDA) and Principal Component Analysis (PCA).
  • Write and test working Python programs from a generic problem statement through algorithm development, design and implementation, unit test, integration, and deployment.
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Note: This course is only open to high school students. 

Course Number: CSE-90160
Credit: 3.00 unit(s)

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