Machine Learning & AI Courses

Understanding the basic functionality of hardware, software, network, and database components is crucial in AI systems. This foundational knowledge is essential for effective implementation of AI solutions and engagement with the technologies.

This course introduces students to the ethical and safety issues arising from the use of Artificial Intelligence technologies, with an emphasis on Responsible AI and proactive solutions.

This course provides professionals, marketers, and entrepreneurs with the strategic and technical skills to harness AI technologies for improving marketing approaches and driving business growth.

This course equips student with the latest knowledge to integrate AI/ML into the product development lifecycle, from model selection to deployment, and to automate and accelerate workflows.

Gain a robust understanding of deep learning through both theory and hands-on implementation, spanning domains such as computer vision, natural language processing (NLP) and graph data analysis. Explore neural network architectures, optimization techniques, and advanced models (CNNs, RNNs, GANs, GNNs). 

This course covers AI fundamentals in finance, Python basics, financial libraries (Numpy, Pandas, etc.), and SQL. It includes machine learning concepts, supervised/unsupervised learning, reinforcement learning, and algorithmic trading, concluding with a real-world case study application.

This course immerses students in the world of generative artificial intelligence (GenAI) and covers high-level browser-based tools, emphasizing the Python code behind them.

Introduction to linear algebra: Systems of linear equations Matrix algebra Linear independence Subspaces, bases and dimension Orthogonality Least-squares methods Determinants Eigenvalues…

Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Students will learn to train a model, evaluate its performance, and improve its performance.

Learn machine learning origins, principles, and practical applications, as well as implementation via the R programming language. Students will learn to train a model, evaluate its performance, and improve its performance.