Introduction to Machine Learning

What Will I Learn?

Unlock the power of machine learning with this foundational certificate designed for data professionals, analysts, and anyone interested in building predictive models. You’ll explore core ML concepts from first principles, gaining an intuitive understanding of cross-validation, bias-variance trade-offs, and essential algorithms like logistic regression, decision trees, and clustering.

By completing this certificate, you will be able to:

• Define fundamental machine learning terms and concepts
• Build and apply common ML models on sample datasets
• Evaluate models using validation and test sets, with appropriate performance metrics
• Clean data and engineer effective features to improve model accuracy
• Use Python libraries like scikit-learn, NumPy, Pandas, and Matplotlib for your ML workflows

Meet Your Instructor

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Yannet Interian is an associate professor in the Master’s in Data Science and Artificial Intelligence program, and her research interests lie in the application of machine learning and deep learning to medical data. She holds a PhD in applied mathematics from Cornell University and a BS in mathematics from the University of Havana, Cuba. After a postdoctoral fellowship at UC Berkeley, she worked for five years as a data scientist at Google. Yannet co-founded Akualab, a start-up that helped organizations develop data-driven products using machine intelligence and has designed data science courses for both UC Berkeley and USF.

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