Fundamentals of Artificial Intelligence Certificate Online: Courses

(**May be removed as prerequisite requirement if the student has the python programming and required libraries background.)

This course covers programming techniques used in AI applications. Topics include programming constructs, I/O, conditional constructs, iterative control structures, structured decomposition, method call and parameter passing, classes, 1-D and 2-D arrays, libraries, APIs, and Data Structures. The course will use Python with several tools where students learn programming with a beginner-friendly introduction to Python and AI libraries including learning how to analyze data, integrate and use basic machine learning algorithms and APIs, create visualizations, implement and test some models, and analyze results.

This course introduces the Python programming language along with data analysis and exploration techniques. Topics covered include the fundamentals of Python programming, visualization, and exploratory data analysis using key libraries such as NumPy, Seaborn, Pandas, and Matplotlib.

This course covers the essential machine learning techniques and algorithms and their applications. Topics include supervised and unsupervised learning, clustering, classification algorithms, linear regression, support vector machines, decision trees, random forests, neural network, deep learning, and reinforcement learning. Throughout the course, students will be exposed to real-world industrial, business, medical and social problems, where the obtained skills are employed to handle data and develop machine learning based solutions. The material and structure of the course are designed with a preference for the practical knowledge of AI more than mathematical or theoretical concepts. Different Machine Learning applications will be discussed including computer vision, natural language processing, time-series prediction, speech recognition, sentiment analysis, cybersecurity, among others.

This course covers principles of deep learning and in its applications. Students will learn how to build and use different kinds of deep neural networks using hands-on approach. Topics include feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers and encoders/decoders. The course will include hands-on applications covering natural language processing tasks, behavioral analysis, financial analysis and anomalies detection.

This course provides a theoretical foundation and practical skills for Generative AI. Topics include anatomy of generative models, transformers, GAN, the Diffusion Model for images. Students will be equipped with the skills to leverage state of the art techniques for visual representation, generative text, text to image synthesis and more.

This course introduces the ETL pipeline: Extract, Transform, and Load. The course provides students with a technical overview on how to source, prepare, and manage data. Students also will be introduced to Dash and to the principles of NoSQL database systems.