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AI-3: Language Models

This course provides you with the fundamental understandings of the latest language models built using deep learning architectures.

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.

This course will prepare you for more advanced studies in deep learning and more intermediate positions in AI research and opportunities.

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6 weeks

Time Investment

10 to 15 hours per week


Live Online


Registration closes

To be decided

Course Outcome

Program Overview

Program Overview

After successfully completing the program, you will be:

  1. Able to build complex language models and gauge their performance.
  2. Awarded a certification in Artificial Intelligence.
  3. Prepared to pursue advanced (graduate and above) level studies in deep learning.
  4. Qualified to seek entry level and intermediate AI and machine learning positions in the industry.

Who should apply

Learners and practitioners who have a fundamental understanding and practice of basic AI and ML concepts including Neural Networks are ideal for this course. You should have strong foundations in statistics, computer science & mathematics.

See if you are ready


Knowledge of beginning and intermediate AI as exemplified by the topics in AI: Basics

Prior knowledge of high level machine learning libraries such as keras

Class Timings

Course Structure

Live online classes
1. Live Online Classes

Bi-weekly live, cohort-based lessons with labs and quizzes

2. Mentor-Supported

Ten or more hours/week to interact with an experienced & accomplished mentor

Module end Project
3. End-of-Module Project

Complex problems that challenge you to apply what you learn

4. Capstone

10–12 week long Capstone Project with one of our partner companies or faculty

Program Faculty

Dr Pavlos Protopapas

Founding faculty Univ.AI

Pavlos is the Scientific Program Director, Institute for Applied Computational Science (IACS) at Harvard University, and leads the data science masters program at Harvard.

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Topics Covered

AI 3

Traditional language modeling
RNNs and LSTMs
Language Modeling (predict next world) with RNNs
Attention and Seq2Seq models
Transformer Models

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