Certificate Programme in Generative AI (Batch-1)

1. Intro to ML: Linear Regression, Logistic Regression 
Learning Outcome: Fundamental ML concepts, understanding regression methods

2. Optimisation Continued, SVM, Decision Tree, Ensemble Methods
Learning Outcome: Learning supervised ML methods

3. Unsupervised Learning: Clustering, Dimensionality Reduction (PCA, LDA, t-SNE)
Learning Outcome: Introduction to unsupervised methods

4. Artificial Neural Networks: Perceptron, Multilayer Network, Backpropagation
Learning Outcome: Understanding the basics of neural networks and their training

1. Basic Text Processing (NLTK, spaCy), Morphology, Stemming, Edit Distance
Learning Outcome: Use of NLP tools for text processing

2. Language Modelling: N-gram Modelling, Smoothing Techniques, Perplexity
Learning Outcome: Learning different language modelling approaches

3. POS Tagging: Sequential Learning, HMM, Viterbi Algorithm
Learning Outcome: Introduction to sequence learning for NLP

4. Parsing: Constituency vs Dependency Parsing, CKY Algorithm, CFG, PCFG
Learning Outcome: Learning techniques for syntactic analysis in NLP

5. Text Classification: Naive Bayes Algorithm, Lexical Similarity (Word Embeddings, TF-IDF, Word2Vec, GloVe)
Learning Outcome: Understanding classification in NLP using traditional and modern approaches
 

1. Neural Language Models (CNN, RNN, LSTM, GRU, Seq2Seq) 
Learning Outcome: Introduction to neural models and attention mechanisms for text generation

2. Attention Mechanism: Self-Attention, Transformer Architecture
Learning Outcome: Understanding self-attention and Transformer architectures for language models

3. Pre-trained Models: BERT, GPT, T5
Learning Outcome: Architecture and training of different pre-trained large language models

4. Fine-tuning Strategies: Task-specific Fine-tuning, Instruction Fine-tuning, Preference Tuning (RLHF, PPO)
Learning Outcome: Learning various fine-tuning approaches to improve model performance

5. Prompting Strategies: In-context Learning, Chain of Thought, Knowledge Probing, Text Generation
Learning Outcome: Techniques for optimising LLM performance through effective prompting

6. Augmented LLMs: Retrieval-Augmented Generation (RAG), Tool Augmented LLM
Learning Outcome: Leveraging RAG and tool augmentation to improve LLM efficiency and reasoning capabilities

1. Vision Language Models (VLM) 
Learning Outcome: Understanding how VLMs enable combined text and image generation for multimodal applications

1. Misinformation, Bias, Toxicity, Security and Fairness
Learning Outcome: Learning strategies to mitigate bias, toxicity, and hallucinations in AI outputs

• Python

• NumPy

• Pandas

• PyTorch

• TensorFlow 

• scikit-learn

• Hugging Face 

• NLTK

• spaCy 

• Jupyter Notebooks
   

• Implement a fully connected neural network using PyTorch or TensorFlow for some task. Train the model and visualise its accuracy and loss over epochs

• Fine-tune a pre-trained transformer model on a text classification task. Evaluate the model on a test set and report its accuracy, precision, and recall.

• Implement a simplified version of the transformer architecture focusing on self-attention and positional encoding. Train the model on a small dataset for translation or another task. Compare the results with a pre-trained transformer model.

• Implement PEFT on a large LLM for a specific task. Compare the training time and resource usage between standard fine-tuning and PEFT.

• Create a simple reward model using human-labelled data for a text generation task (e.g., summarisation or sentiment correction). Implement a reinforcement learning loop to improve the LLM's responses based on the reward model.

• Compare the performance of different open-source Large Language Models (LLMs) on reasoning tasks using various prompting techniques—zero-shot, few-shot, chain-of-thought, self-consistency prompting.

• Compute matrix operations using NumPy

• Preprocess a text dataset and generate embeddings using spaCy or Hugging Face

• Build a simple neural network using TensorFlow/PyTorch.

• Train the model on basic NLP tasks like  text classification problem.

• Implement a simplified self-attention mechanism.

• Use a pre-trained transformer models for various NLP tasks like translation or summarization.

• Fine-tune a GPT or BERT model on a custom dataset for various NLP problems

• Use Hugging Face's pre-trained transformer model to generate text.

• Apply PEFT to a large model for fine-tuning on a small dataset.

• Apply quantization and pruning on a pre-trained model to optimize for lower resource usage.

• Implement a RAG model using Hugging Face transformers and document retrieval from a custom corpus.

• Use Hugging Face multilingual models for problems in cross-lingual scenarios.

Any graduates or postgraduates in Science, Technology, Engineering or Mathematical Sciences are eligible to apply for this IIT Delhi Certificate Programme in Generative AI.

The IIT Delhi Certificate Programme in Generative AI is ideal for AI/ML professionals looking to deepen their expertise in Generative AI and Large Language Models (LLMs). It is also well-suited for Data Scientists and Engineers eager to advance their skills in natural language processing and deep learning techniques. Tech leaders and managers can benefit from the program to drive AI innovations within their organisations. Product Managers and innovators focused on AI-driven product development will find the course valuable for integrating advanced AI solutions. Additionally, academics and researchers can explore the latest AI advancements and their real-world applications, while software developers interested in NLP and model fine-tuning will gain practical insights. Lastly, AI enthusiasts seeking to transition into the AI/ML field will gain hands-on experience in cutting-edge methodologies.

The IIT Delhi Certificate Programme in Generative AI course curriculum consists of Mathematical Foundations for ML, Machine Learning, NLP, Generative AI for Text and Vision, Responsible AI, Projects and more.

The duration of this IIT Delhi Certificate Programme in Generative AI is 6 months.

To enrol in the IIT Delhi Generative AI course, simply click on the “Enrol Now” button and provide your details. Our program advisors will reach out to you for any clarification or assistance needed to complete the registration process. Please ensure you meet the eligibility requirements. If admissions are closed or to be announced, kindly fill out the Enquiry Form by clicking the “Enquire Now” button, and our program advisors will get in touch with you to provide further information and updates.

Upon completing the IIT Delhi Certificate Programme in Generative AI, you can explore a wide range of career opportunities in AI-driven roles. These include positions such as AI/ML Engineer, Data Scientist, and Natural Language Processing (NLP) Specialist, where they can apply their expertise in Generative AI and Large Language Models (LLMs). Other roles include AI Researcher, AI Product Manager, and AI Solution Architect, focusing on integrating AI technologies into products and services. Tech leaders and managers can transition into Chief AI Officer or AI Strategy Consultant roles. Additionally, professionals may move into AI Ethics and Policy roles, ensuring responsible AI deployment. Overall, the program opens pathways across various industries, including tech, healthcare, finance, and e-commerce.

The IIT Delhi Certificate Programme in Generative AI offers significant scope in India, a rapidly growing hub for AI innovation. As the country increasingly adopts AI across industries like healthcare, finance, e-commerce, and manufacturing, the demand for skilled professionals in Generative AI and Large Language Models (LLMs) is rising. This program provides participants with cutting-edge knowledge in areas like NLP, reinforcement learning, and AI model optimization, making them highly valuable in roles such as AI Engineer, Data Scientist, and AI Researcher. The course’s practical focus on real-world applications also positions graduates to drive AI-driven innovations within organisations. With India’s push toward digital transformation, the program provides an excellent opportunity for professionals to capitalise on the expanding AI job market.

Choosing IIT Delhi for the Generative AI course offers several compelling advantages. As one of India's premier institutions, IIT Delhi is renowned for its rigorous academic standards and cutting-edge research in AI and technology. The program provides access to world-class faculty, who are experts in AI, machine learning, and deep learning. Participants gain hands-on experience with state-of-the-art techniques like Large Language Models (LLMs), reinforcement learning, and AI optimization. IIT Delhi’s strong industry connections and focus on real-world applications ensure that learners are equipped with the skills needed to meet the growing demand for AI professionals. The course’s practical approach, combined with IIT Delhi's legacy of excellence, positions graduates for success in the rapidly evolving AI landscape.

Students can request a withdrawal within 15 days from the programme start date, and they will receive a refund of 80% of the total fee paid. However, taxes paid on the amount will not be refunded. If a withdrawal is requested after these 15 days, no refund will be applicable. To initiate a withdrawal, please send an email stating your intent to withdraw to cepaccounts@admin.iitd.ac.in and icare@timespro.com.