Here is the complete listing of all our individual courses. You can take some or all of these courses as a part of our Master and Fast Programs, and our micro-certifications. A few courses are also offered staff alone, which you can take to augment your knowledge in specific areas.
This course is the first part of our AI-Y: Expert Micro-Certification. This is an advanced course to teach you NLP. You will start with the traditional language models, learn about word embeddings, attention and then move on to transformer models. At the end of this module, you will be able to build efficient language models, and tell how well they are performing.
This course is designed to build a strong foundation in Data Science and Machine Learning. The course proceeds in 3 parts, following the Data Science Process and covers - Obtaining, processing and cleaning data; Exploratory Data Analysis; and Modelling, including classification and recommendation engines.
This course builds on the fundamentals taught in ML-1. We continue our modeling journey from DS-1 with tree-based models: these have proved to be the best models outside of deep learning. Specifically, we focus on Random Forests and Boosting. We then move on to key Bayesian and other statistical concepts that help us qualify the uncertainty in the prediction of our models, and help us make decisions.
The objective of this module is to provide fundamental understandings of machine learning models and get you working with the basic concepts of ML and AI. You will start with the regression models (KNN, Linear, Multi, Poly) and then move on to classification models (kNN, Logistic). Finally, the course will provide a basic understanding of modern neural networks.
This course is a follow up to AI-1. The objective of this module is to provide you, the student, with a deeper intuition and understanding of neural networks including network architecture choices, activation functions, feed-forward, convolutional neural networks and auto-encoders. At the end of this course, you will be able to run a variety of advanced machine learning models, and learn to apply them to practical image recognition problems.
The objective of this advanced course is to provide fundamental understanding of the concepts behind Generative Models and Reinforcement Learning and apply them to real world problems. The first half of the course starts with generative models and covers autoencoders, variational autoencoders and Generative Adversarial Networks. The second half of the course takes you to Reinforcement Learning.
This is a 12-16 week Capstone project or Internship for those who have completed AI Y, or have equivalent preparation to join this course directly. At the end of AI Z you will be proficient at learning and applying cutting-edge skills to solve real-world problems. You will be well prepared for top employment opportunities worldwide. This course is available as a stand alone micro-certification with a state-of-the art-employment guarantee for high scorers. Direct admission (for those who have not taken the AI-Y module) with employment guarantee is via an examination and interview.
We welcome learners from all academic backgrounds - students or working professionals. Our X, Y and Z level programs suit the learning needs of beginners, intermediate learners and expert learners, respectively.
Univ.AI offers a series of micro-certification, each of which has its own individual outcomes.
You can take these programs consecutively or at your own pace, with breaks in between programs.
Yes, you may. All our programs are standalone certification programs to provide you with specific professional outcomes. You can proceed to the next level of the series if you want a greater professional outcome, and can commit the time needed. Each program is 10 to 12 weeks long.
You can review recorded videos which are available soon after a class. You can seek help if you need any during your daily live mentor sessions.
Our alums work at Microsoft, Cisco, Mindtree, Deloitte and several prominent startups. A significant number of our alums choose to attend graduate school at top institutions. Recently our alums have gained acceptance to Carnegie Mellon, Harvard, UCLA, University of Michigan, Georgia Tech and University of British Columbia among others.
With all our programs, you get 5 to 6 hours of LIVE online mentor sessions scheduled per week. If you need further assistance, you can request for more, asynchronous sessions with your mentor.
The Level-Z program for both machine learning and AI tracks is a hands-on internship / a Capstone project where you will work with Univ.AI’s partner companies or institutions to apply advanced skills.