Professional Certificate Programme in Generative AI and Machine Learning

• Python basic data structures, Lists, Tuples, Sets, Dictionaries, Functions, and Loops

• Control Structures, File Handling, Comprehensions OOPs, Generators, and Libraries

[Taught by Domain Experts]

• Excel based (Importing, Grouping, Pivots), SQL based (Aggregation), Git Fundamentals, Collaboration and Version Control

• Python for data science (Numpy, Pandas, Matplotlib, SciPy, Scikit-learn, etc.)

[Taught by Domain Experts]

• Data cleaning, feature selection, and normalization

• Hands-on exercises, case studies, and discussions

[Taught by Domain Experts]

• Linear regression and evaluation metrics

• Multiple, Polynomial, Overfitting, solution to Over Fitting

[Taught by IIT Faculty and Domain Experts]

• Evaluation metrics

• Logistic regression, Decision tree, Random Forest, SVM, Model Deployment basics (store, load, predict)

[Taught by IIT Faculty and Domain Experts]

• Basics, distance matrix and applications

• How to implement clustering (Agglomerative clustering) and connect with business requirements, Algorithms (PCA, CFA), Association Rule Mining, DB Scan, and Anomaly Detection (Nearest Neighbor and Isolation Forest)

[Taught by IIT Faculty and Domain Experts]

• Ensemble technique with examples (its difference from supervised and unsupervised learning); types (Bagging and boosting )

• Bagging and boosting and different algorithms; Libraries (Adaboost, GB, XGB, Catboost)

[Taught by IIT Faculty and Domain Experts]

• Types of timeseries data, AR and MA Modelling

• ARIMA, FB Prophet, and implement data

[Taught by IIT Faculty and Domain Experts]

• Cross-validation, neural networks, activation functions, and DL frameworks

• Cross-validation, neural network coding, and applications

[Taught by IIT Faculty and Domain Experts]

• Perceptrons, math behind perceptrons, and Python implementation

• Introduction to MLPs, forward propagation, Python implementation, Introduction, math derivation, and Python implementation

[Taught by IIT Faculty and Domain Experts]

• Introduction to optimisers, activation functions, loss functions, Overfitting scenario

• Best practices in choosing optimisers, activation functions, loss function, batch normalization and dropout technique

[Taught by IIT Faculty and Domain Experts]

• Convolution Neural Network (Filters - Feature Detectors, Pooling - Avg, Max, Padding and Stride); Basic Architecture

• Pre-trained networks, transfer learning, and fine tuning

[Taught by IIT Faculty and Domain Experts]

• Recurrent Neural Networks (Temporal Nature of Data, Recurrent Mechanism, Types of RNN), LSTM Gates, and GRU Gates

• Applications, Drawbacks of RNN, LSTM and its drawbacks, GRU. Attention Mechanism, and Transformers

[Taught by IIT Faculty and Domain Experts]

• Autoencoders, DBN, and RBM

• Applications of networks for various use cases

[Taught by IIT Faculty and Domain Experts]

• Introduction to NLP, Text Preprocessing, Text Tokenization, and Word Embeddings

• Text Classification, Use of Sequence models (RNN, LSTM, GRU), NER, Information Extraction, and Machine Translation

[Taught by IIT Faculty and Domain Experts]

• VAE, GAN, Architecture, training process of generator and discriminator, DCGAN, WGAN and other GANs—introduction and sequence generation

• Implementation and application

[Taught by IIT Faculty and Domain Experts]

• Attention mechanism; transformer architecture; BERT (Bidirectional Encoder Representations from Transformers); ViLBERT; GPT (Generative Pre-trained Transformer) and applications of transformer models

• Applications of BERT /VilBERT and transformer models

[Taught by IIT Faculty and Domain Experts]

• Applications of Generative AI in different domains

• Examples of prompt engineering, fine tuning, API creation, and integration

[Taught by IIT Faculty and Domain Experts]

• Fine-tuning, transfer learning, prompt engineering, applications, other LLMs, RAG architecture, frameworks for RAG implementation, and building RAG based apps

• Prompt engineering, vector databases (FAISS, Chroma Db), and building applications based on RAG

[Taught by IIT Faculty and Domain Experts]

• Introduction to Agentic AI, core concepts in LLM-powered Agentic AI - agent architecture

• Hands-on exercises

[Taught by IIT Faculty and Domain Experts]

• Markov Decision Processes (MDPs); Q-Learning and Deep Q Networks (DQN); Actor-Critic models; exploration vs. exploitation strategies

• Focus on applications of reinforcement learning

[Taught by IIT Faculty and Domain Experts]

• The capstone project is a comprehensive, real-world assignment in which participants apply their knowledge and skills to solve industry-specific problems

• It integrates concepts from their coursework, encouraging critical thinking and innovation

• Capstone projects help participants gain hands-on experience, making them industry-ready by demonstrating their ability to tackle complex challenges in a professional setting

• Self-Supervised Learning Techniques, Video Analysis, Basic Image Processing Techniques, Self-Supervised Techniques demo 

• Object Detection Algorithms, Image Segmentation, and Explainable AI (XAI); R-CNN, Fast R-CNN, Faster R-CNN, YOLO, Single Shot Detection (SSD), and their implementation 

The generative AI course explores Agentic AI, Retrieval Augmented Generation (RAG), and practical applications of LLMs. Learners work with tools such as TensorFlow, Scikit-Learn, and Gradio, alongside completing real-world projects.

Yes, the programme is ideal whether you are beginning your journey with generative AI learning or are a professional seeking advanced knowledge. It serves as an excellent choice among genAI courses for beginners, offering practical, industry-focused training.

Absolutely. This genAI programme offers comprehensive coverage of machine learning and AI topics, including deep learning AI, traditional ML algorithms, and the latest innovations in Generative AI.

Yes, upon successful completion, participants are awarded a prestigious certificate in AI and ML from IITM Pravartak. This certificate bolsters your credentials for genAI certification programmes and enhances employability across global markets.

Learners may specialise in either Advanced LLMs (Large Language Models) or Computer Vision. These options enable you to focus on in-demand areas within AI and ML, aligned with evolving industry needs.

Certainly. Through 15+ tools and libraries and 15+ practical projects, the programme offers robust machine learning training in virtual labs, ensuring real-world experience and industry readiness.

The course is delivered by distinguished IIT Madras faculty, including Prof. Ganapathy Krishnamurthi and Prof. C. Chandra Sekhar. They bring immense academic and practical expertise to the AI training courses, ensuring a world-class learning experience.

Yes. The programme includes a two-day optional immersion event at the IIT Madras Research Park, providing valuable hands-on experience, networking opportunities, and direct interaction with faculty — a key highlight of the AI ML certification courses.

After completing the generative AI programme, you will be well-positioned for roles such as AI Engineer, Machine Learning Specialist, Data Scientist, and Generative AI Developer. With expertise in AI and ML, including hands-on experience with deep learning AI and LLMs, you will meet the growing demand across industries such as finance, healthcare, technology, and consulting.

Yes, there is an optional two-day campus immersion available to learners at the IIT Madras Research Park where they will get to meet the programme faculty and generative AI and ML experts on the campus.

The Generative AI and Machine Learning Programme by IITM Pravartak is a course that focuses on the primary application of Generative AI and ML. At just 7 months, the course is designed to help learners grasp advanced implementations of the GenAI through dedicated projects, tools, specialisations, and use cases for business use. On the other hand, the AI, Machine Learning, and Deep Learning course by IITM Pravartak is a deep dive into the technical applications of AI from baseline. The course is longer for more comprehensive learning and includes concepts from GenAI and Agentic AI. However, its primary focus is to impart a fundamental understanding about how AI, ML, and deep learning work.