Formerly, Harvard University
Scientific Program Director,
IACS, Harvard University
PhD, University of Pennsylvania
Former Professor, Harvard University
Computational Scientist & Cosmologist
PhD, University of Pennsylvania
Incoming graduate student at Harvard University
"Univ.AI gave me a structured learning environment. The program helped me understand why one algorithm worked better than another for a given application. The quality of the peer group and the personalized time devoted by the professors are two things that I really helped me gain conceptual clarity”
Phd Mathematics, Technical University, Munich
“The learning experience at Univ.AI is highly engaging, interactive and lively. It is most definitely on par with the best universities in the world. I especially enjoyed the teaching style of Dr. Protopapas and the high level of care and dedication displayed by the academic team.”
Data Scientist at Glance, Ex- VMware
“My growth has been tremendous! All the sessions and assignments that I've done as part of this course are extremely relevant in real world applications. In fact these skills, along with the amazing peers that I met in this course helped me in seemless transition from my Software Developer role at VMware to Data Scientist role Glance. My new role requires me to pick up new tools quickly, and I think its because of my training at Univ.ai that I can adapt with great ease."
Machine Learning Lead at Metro Services
“The mentorship we got during the program was a perfect complement to learning from top faculty. The curriculum is challenging, but for the committed students the learning experience is exceptional. I was surprised at the pace at which I was able to develop my expertise with Univ.AI.”
Start with the basics, and become an ML/AI expert in 10 months. The program guarantees sought after jobs to top performers. If you are not familiar with python programming, basic mathematics and statistics, then we recommend you complete Python for Data Science [PyDS] as a bridge course before AI-1 starts.
AI-1: AI Basics
You will become familiar with and gain expertise in Supervised Learning models including regression models (KNN, linear, multi, poly) and classification models (KNN, Logistic). You will then learn about Modern Neural Networks.
DS-1: Data Science Basics
You will learn how to get, clean, and process data from different sources. You will then gain skills in exploratory data analysis, visualization, and communication. You will learn to build classification and recommendation engines.
AI-2: Convoluted Neural Networks
Continue your data science journey with convolutional neural networks. Obtain a deeper intuition with network architecture choices, activation functions feed forward and auto encoders. At the end of this course, you will be able to run advanced machine learning models and apply them to practical image recognition problems.
AI-3: Language Models
This is an advanced course for developing proficiency with Natural Language Processing. 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 course, you will be able to build efficient language models, and tell how well they are performing.
DS-2: Data Science II
You will develop your ability to use generative models and clustering. You will learn about text and tree models, ensembles, recommendation systems, clustering, and Bayesian Statistics.
AI-5: Productionizing AI (MLOps)
This is a 8 weeks (plus 4-6 weeks of extended project) hands-on course on industrial AI concepts & practices. This advanced course is ideal for those who have completed AI-3, AI-4A or AI-4B, or have equivalent preparation to join this course directly. At the end of the course, you will be proficient at applying cutting-edge skills to solve real-world problems. You will be well prepared for top employment opportunities worldwide. We guarantee top-tier placements to exceptional performers in the program (who complete the program with an ‘A’ grade). Direct admission (for those who have not taken AI-3, AI-4A or AI-4B) to the course is through an application followed by an interview.
AI-4A: Reinforcement Learning(Elective)
This advanced course will provide a fundamental understanding of the concepts behind Reinforcement Learning and how to apply them to real-world problems. The course covers the basic concepts, dynamic programming, Q-learning and Policy Gradient Methods. At the end of this module, you will be able to efficiently work with reinforcement learning problems.
AI-4B: Generative Models(Elective)
This advanced course will give an overview of network building blocks, followed by a review of Generative Adversarial Networks and their applications. The course also touches on latent space interpretation. At the end of this module, you will be able to build effective generative adversarial networks.
At the end of the program, you will be a master in creating AI & ML models in any domain you choose to pursue, such as medicine, finance and e-commerce.
Exceptional performers (those who score grade "A" aggregate) get a guaranteed state-of-the-art placement in a top-tier organisation.
This LIVE-online, part-time program is for college or university students and working professionals who seek the most challenging and rewarding careers in ML and AI and are prepared to invest time and effort to master this field. You need to have the requisite knowledge of python programming, basic statistics and mathematics.
If you need to study or revise the pre-requisites, we recommend you complete Python for Data Science [PyDS] as a bridge course before AI-1 starts.
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