• 1 Hour

  • 4.7/5

  • Beginner

Course Description

Mastering Multilingual GenAI – Open-Weights for Indic Languages" is a course designed to equip you with the knowledge to develop state-of-the-art multilingual AI models using open-weight architectures. Focusing on low-resource languages, particularly Indic languages, the course covers essential topics like multilingual AI training, instruction fine-tuning, model building, and performance evaluation.

Course curriculum

  • 1
    Mastering Multilingual GenAI
    • Introduction
    • Importance of Multilingual
    • Training for Multilingual Gen AI
    • Instruction Fine-Tuning Data for Multilingual
    • Measuring Performance for Multilingual
    • Building a Model
    • Human Preferences
    • Curse of Multilinguality
    • Coding Hands-On

Certificate of Completion

Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Certificate of Completion

Who should Enroll?

  • Those looking to build state-of-the-art multilingual models for low-resource languages.

  • Tech Entrepreneurs and Innovators who want to build scalable, inclusive AI systems that cater to a diverse, multilingual user base.

  • Data Scientists seeking to explore state-of-the-art instruction fine-tuning techniques and work with large multilingual datasets.

  • Students and Professionals aspiring to work in cutting-edge AI fields, with a specific interest in bias mitigation, safety, and ethical considerations in language models.

About the Instructor

Viraat Aryabumi - Research Scholar at Cohere for AI

Viraat is a Research Scholar at Cohere for AI, where he contributed to the Aya Project and Aya-101 model. He previously 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.
LinkedIn
About the Instructor

FAQs

  • What prior knowledge is required for this course?

    A basic understanding of AI and machine learning concepts is recommended. Familiarity with natural language processing (NLP) will be helpful but is not mandatory.

  • What tools and technologies will I learn in this course?

    You'll work with state-of-the-art open-weight models, instruction fine-tuning techniques, and the Aya dataset.

  • How does this course address low-resource languages?

    The course focuses on training models that perform well on low-resource languages, particularly Indic languages, through the use of the Aya dataset, multilingual instruction fine-tuning, and bias mitigation techniques.

  • Will there be hands-on projects?

    Yes, the course includes practical coding exercises and real-world projects where you will build and fine-tune multilingual AI models, with a focus on tackling real-world challenges in low-resource language modeling.

Key Takeaways

  • Learn to create AI models for diverse languages, focusing on low-resource ones.

  • Get hands-on experience with cutting-edge models and multilingual data.

  • Implement strategies for safer, unbiased AI models in global applications.