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.

This course teaches students to design and deploy Retrieval-Augmented Generation systems, combining LLMs with external data for accurate, scalable AI applications using modern tools, evaluation frameworks, and cloud platforms.

This course provides a comprehensive exploration of computer vision and deep learning, equipping students with Python proficiency, image processing skills, and advanced neural network techniques to tackle real-world applications in healthcare, security, robotics, and automation through AI-driven solutions.

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 introduces generative AI through theory and hands-on practice, covering model evolution, practical techniques, and ethical frameworks to build, refine, and evaluate systems in real-world contexts.

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.

This course explores the theoretical concepts, design principles, and practical applications of large language models (LLMs). Students will gain a deep understanding of how LLMs process and generate…

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

This course provides an in-depth exploration of machine learning systems design, covering the complete lifecycle from project scoping and data acquisition to model deployment and monitoring. We…

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.

Computer programs that process human language are now a part of everyday life. The branch of artificial intelligence called natural language processing (NLP) has enabled the development of chatbots,…

Join our interactive webinar on Trustworthy Machine Learning to explore AI reliability, fairness, and GenAI security threats, covering key concepts, real-world risks, and expert-led case studies with Q&A, all without requiring coding experience.

Get equipped with theoretical and practical skills to build trustworthy machine learning systems, focusing on generative AI, model reliability, safety, privacy, fairness, and compliance through hands-on projects using industry-standard tools.