Specialist Certification in AI & ML
Course Duration
9 months
Time Commitment
10 Hours/Week
Eligibility
12th Pass and Above
Learning Mode
Online
3.85K+ students have already registered
Course Duration
9 months
Time Commitment
10 Hours/Week
Eligibility
12th Pass and Above
Learning Mode
Online
Why Choose This Course?
Learn Like an IITian, Excel Like a Global Leader
Receive a Certificate of Completion with Vishlesan i-Hub, IIT Patna, significantly enhancing your profile's value.
Work on capstone projects designed to solve practical business challenges.
Learn from IIT faculty & industry experts bringing real-world insights to your learning journey
Receive resume reviews, career coaching, and placement support to land your dream role. (Eligibility: 7+ CGPA and 65% attendance)
Prestigious Certification*:
Receive a Certificate of Completion with Vishlesan i-Hub, IIT Patna, significantly enhancing your profile's value.
Real-World Capstone Project:
Work on capstone projects designed to solve practical business challenges.
Expert Faculty & Industry Insights:
Learn from IIT faculty & industry experts bringing real-world insights to your learning journey
Placement Opportunities
Receive resume reviews, career coaching, and placement support to land your dream role. (Eligibility: 7+ CGPA and 65% attendance)
*Certificate Eligibility: Minimum 65% overall attendance and 30% marks in cumulative evaluations score
What Will You Learn?
An intensive, practitioner-focused program — from Python fundamentals to production-grade ML, LLMs, and agentic AI systems.
Toolkit
Course Details
Duration
9 Months
Course Mode
Online
Certification
from Vishlesan i-Hub IIT Patna
- • Write Python for data work — variables, loops, functions, and data structures built with ML use cases in mind
- • Perform structured EDA: univariate and bivariate analysis, correlation checks, and leakage detection
- • Clean and reshape real-world messy datasets using pandas — missing values, dtypes, merges, groupbys
- • Build the mathematical intuition — linear algebra, probability, gradient descent intuition, and loss functions
- • Set up a professional dev environment with Git, GitHub, Jupyter, and reproducible notebook discipline
- • Build and evaluate all major ML model families — regression, classification, clustering, and tree-based models
- • Understand model assumptions deeply: when they break, what to do, and how to diagnose failure modes
- • Go beyond accuracy — learn precision, recall, confusion matrices, ROC-AUC, and when each metric matters
- • Build end-to-end feature pipelines with cross-validation, hyperparameter tuning, and experiment tracking
- • Interpret model decisions using SHAP and understand reproducible, production-ready ML workflow design
- • Build MLPs from scratch — forward pass, backpropagation, optimisers, regularisation, and training diagnostics
- • Master CNNs for vision tasks and RNNs for sequences, with full transfer learning workflows using pretrained models
- • Understand attention mechanisms and get a working intro to transformer architecture from the ground up
- • Work across both modalities — text preprocessing, sentence embeddings, classification, and CNN-based image models
- • Debug training runs properly, learning curves, gradient issues, overfitting patterns, and evaluation on held-out sets
- • Understand transformer internals — self-attention, positional encoding, pretraining, RLHF, and inference behaviour
- • Design production-grade prompt architectures: system prompts, structured JSON outputs, and function calling
- • Build and evaluate LLM application flows with prompt testing, eval datasets, and safety boundary design
- • Understand context windows, tokenisation tradeoffs, and how model behaviour shifts with scale and fine-tuning
- • Go beyond simple prompts — orchestrate tool use, multi-turn flows, and user experience design for LLM apps
- • Build production-grade RAG with chunking strategies, metadata filtering, hybrid retrieval, reranking, and query rewriting
- • Evaluate and stress-test RAG pipelines — citations, failure analysis, latency benchmarking, and cost modelling
- • Learn practical fine-tuning workflows: dataset curation, instruction data design, supervised fine-tuning, and LoRA
- • Know exactly when fine-tuning boosts prompting and when RAG wins — with evaluation frameworks to prove it
- • Build agentic systems with tool calling, stateful memory, conditional routing, and human-in-the-loop design
- • Serve ML models as live REST APIs with FastAPI — serialisation, versioning, endpoints, and structured logging
- • Containerise and deploy with Docker to cloud platforms with environment parity from dev to production
- • Build reliable GenAI workflows with prompt versioning, tracing, eval sets, cost tracking, and latency monitoring
- • Implement guardrails, fallback behaviour, and user feedback loops that keep GenAI apps safe in production
- • Engineer every project to professional standards — branching, PRs, reproducibility, and portfolio-ready codebases
Projects
Product-Image Classifier (CNN)
Flipkart’s catalog team wants to auto-categorise product images into Apparel, Electronics, and Home. You train a small CNN on a public Indian e-commerce image dataset using free Colab GPU, push validation accuracy past 85%, and produce a confusion-matrix breakdown that names exactly which category pairs the model still fumbles — and why.
DECISION OUTPUT
Trained classifier with documented confusion patterns the catalog team can route to manual review.
Customer-Churn Predictor & API
Build a churn-prediction model for a D2C customer dataset. Engineer RFM features (recency, frequency, monetary), train a gradient-boosting classifier, deploy as a FastAPI endpoint, and write the model card — including the ethical considerations around retention targeting that any real product team will probe in your interview.
DECISION OUTPUT
A deployable churn predictor with a documented model card you can defend to a hiring panel.
Please Note :- "Due to the evolving nature of the industry expectations and partner institute feedback, some syllabus aspects may change. Any updates will be communicated during the Inauguration Session(s) or at the start of the relevant module". The project deliverables or case studies for your batch may differ, as per the decision of the Institute or Course Faculty
Meet Our Instructor Team
*Note: Instructors teaching your batch may change based on specific module expertise, or circumstances such as personal emergencies, unavailability etc.