Beginner AI/ML Foundations

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This course, part of the Master in Applied Artificial Intelligence program, covers the fundamentals of theoretical statistics that form the foundation for analyzing machine learning algorithms. Topics include statistical models, inference, maximum likelihood estimation, hypothesis testing, and Bayesian inference.

A graduate-level course in machine learning with a focus on fundamental methodologies and algorithms, including Kernel Methods and Support Vector Machines.

This course emphasizes the importance of building reliable machine learning systems. It covers software testing basics applied to the ML domain to enhance the quality of ML applications. The curriculum includes different testing methodologies like unit and integration testing, as well as more advanced techniques designed for machine learning such as behavioral and smoke testing.

This course provides a foundational understanding of what data analytics is and the role of a data analyst. It covers topics like thinking like an analyst and gathering useful data.

This course provides a foundational understanding of linear regression, one of the most important algorithms in machine learning and AI. It covers the theory and practical implementation of linear regression.

This intermediate-level course explains how to create one of the most common types of machine learning: supervised learning models.

This course introduces Elasticsearch, focusing on the basic building blocks of search algorithms and the underlying data structures. It covers installation, indexing, performing various types of search queries, and exploring the TF/IDF algorithm for search ranking and relevance.

Artificial Intelligence Foundations: Machine Learning

Machine Learning and AI Foundations: Value Estimations

Quantum Machine Learning: Theory and Applications

"This course contains the use of artificial intelligence in creating scripts, visuals, audio, and supporting content"This 8-week course is a complete foundation in Generative AI and Large Language Models (LL Ms), designed to help you build both conceptual understanding and practical skills. The program is structured to gradually move from the basics of generative models to advanced applications, customization, safety, and a capstone project that showcases your abilities. The course begins with an introduction to Generative AI, where you will explore tokenization, attention mechanisms, and the transformer architecture that forms the backbone of modern LL Ms. You will learn how text generation works, experiment with prompt design, and analyze the impact of model parameters like temperature and top-p on creativity and accuracy. Building on this, the course dives into the foundations of large language models, exploring embeddings, perplexity, and context windows. You will also study core generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models, gaining an intuitive understanding of how these models generate text, images, and structured data. The practical modules allow you to apply Generative AI in practice, including summarization, creative writing, code generation, data augmentation, and image synthesis. You will use modern <strong