Mastering Multilingual GenAI Open-Weight for Indic Language

BeginnerLevel
287+Students Enrolled
1 HrDuration
4.6Average Rating

About
Course Benefits
Learning Outcomes
Who Should Enroll
Course Curriculum
Instructors

About this Course

Develop multilingual AI with open weight models for low resource Indic languages and ethical, responsible use.
Train and fine tune Indic multilingual models to improve accuracy, inclusivity, and coverage in real world apps.
Use high quality instruction data and human preference alignment to train multilingual AI for diverse communities.
Build fair, low bias multilingual AI so Generative AI with multilingual models can be deployed safely worldwide.

Course Benefits

Work with Generative AI multilingual models focused on Indic languages.
Gain insight into evaluation, human preference alignment, and multilinguality effects.
Build a strong foundation for multilingual NLP tutorials and future research.
Understand how to train multilingual AI models using open weight approaches.

Learning Outcomes

Build Multilingual AI

Create AI for diverse Indic languages using open weights.

Hands On Training

Train open weight models on multilingual data and tune.

Fair and Safe AI

Apply bias mitigation and inclusive design for safer AI.

Who Should Enroll

Developers who want to build multilingual AI for low resource Indic languages using open weight models.
Tech innovators building scalable multilingual AI systems for diverse Indic and global language users.
Data scientists exploring instruction fine tuning, evaluation, and bias mitigation in multilingual GenAI.

Course Curriculum

Multilingual deep learning course on Generative AI for Indic languages, covering open weight training, instruction data, evaluation, curse of multilinguality and coding labs.

Understand why multilingual GenAI matters, especially for Indic and low resource languages. Learn how training for multilingual GenAI works, how instruction fine tuning data is prepared, how performance is measured across languages, and how human preferences and the curse of multilinguality influence model design and deployment.

1. Introduction
2. Importance of Multilingual
3. Training for Multilingual Gen AI
4. Instruction Fine-Tuning Data for Multilingual
5. Measuring Performance for Multilingual
6. Building a Model
7. Human Preferences
8. Curse of Multilinguality
9. Coding Hands-On

Meet the instructor

Our instructor and mentors carry years of experience in data industry

Viraat Aryabumi

Research Scholar, Cohere

Viraat is a Research Scholar at Cohere for AI, where he contributed to the Aya Project and Aya-101 model. led Machine Learning at Aiara and was a Machine Learning Scientist at Amazon. He holds a Master's in AI from the University of Edinburgh.

Get this Course Now

With this course you’ll get

1 Hour
Duration
Viraat Aryabumi
Instructor
Beginner
Level

Certificate of completion

Earn a professional certificate upon course completion

Industry-Recognized Credential
Career Advancement Credential
Shareable Achievement

Frequently Asked Questions

Looking for answers to other questions?

A basic understanding of AI and machine learning concepts is recommended, such as what models, training, and evaluation mean in general. Familiarity with natural language processing helps but is not mandatory, since key ideas are introduced in a beginner friendly way. The course gradually connects these ideas to Generative AI with multilingual models.

The course works with state of the art open weight models designed for multilingual use and highlights the Aya dataset as a core multilingual resource. Concepts such as instruction fine tuning, multilingual evaluation, and preference alignment are discussed, giving a practical view of how to train multilingual AI models in modern ecosystems.

A central focus of the course is low resource Indic languages and how Generative AI with multilingual models can support them. Training strategies based on multilingual instruction data, the Aya dataset, and careful evaluation are discussed. Bias mitigation and fairness are emphasized so multilingual AI can serve communities that are often underrepresented in traditional NLP pipelines.

The course includes practical coding segments where multilingual data is used with open weight models. Examples show how to fine tune, evaluate, and inspect behavior on Indic languages. This makes the course more than theory and turns it into a multilingual NLP tutorial that learners can extend into their own experiments and projects.

The course connects theory with practice by showing how Generative AI with multilingual models can support search, chatbots, content creation, and language tools for Indic and global languages. Real world examples highlight how multilingual AI systems handle mixed language input, regional context, and fairness constraints in production style settings.

The course is designed as a beginner level multilingual deep learning course, so heavy math or advanced research background is not required. Basic familiarity with AI and machine learning is enough to follow the explanations on training multilingual AI models, instruction fine tuning, performance measurement, and bias considerations for multilingual systems.

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