Advanced Certification in AI & ML
Course Duration
7 months
Time Commitment
10 Hours/Week
Eligibility
12th Pass and Above
Learning Mode
Online
3.7K+ students have already registered
Course Duration
7 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?
Learners will develop end-to-end AI and Machine Learning expertise, progressing from Python programming and data analysis to deep learning, generative AI, and production deployment. The programme emphasises practical implementation through real-world projects, modern AI frameworks, and industry-standard development workflows.
Toolkit
Course Details
Duration
7 Months
Course Mode
Online
Certification
from Vishlesan i-Hub IIT Patna
- • Write Python the way data professionals do — clean, structured, and reusable
- • Work with real datasets using NumPy and pandas: clean, reshape, filter, and summarise
- • Pull data from APIs and explore it visually to find patterns before modelling
- • Build the mathematical intuition — probability, stats, gradients — that makes ML click
- • Set up a professional dev environment with Git, GitHub, and notebooks discipline
- • Learn the major ML families — regression, classification, clustering, tree-based models
- • Understand model assumptions, when things go wrong, and how to fix them
- • Validate models properly so results hold up on data the model hasn't seen
- • Build end-to-end pipelines from raw data to a trained, evaluated model
- • Interpret what your model is actually doing and why it made a prediction
- • Understand how neural networks learn — forward pass, loss, backpropagation, optimisers
- • Build image models using CNNs and get results faster with transfer learning
- • Work with text: clean it, embed it, and build classification models on top
- • Understand the difference between vision and language tasks and when each applies
- • Move from classical ML to deep learning knowing exactly what you're gaining and trading off
- • Understand how LLMs work — tokenisation, attention, and why they behave the way they do
- • Write prompts that produce structured, reliable outputs for real applications
- • Build RAG pipelines so models can reason over your own documents and data
- • Know when to fine-tune, when to prompt, and when RAG is the right call
- • Create simple AI agents that can use tools and complete multi-step tasks
- • Wrap your models in a REST API and serve them as a live, callable service
- • Containerise your app with Docker so it runs the same everywhere
- • Deploy to the cloud and understand what it takes to keep a model running in production
- • Handle GenAI production realities: hallucination, latency, cost, and prompt drift
- • Version-control everything so your projects are reproducible and portfolio-ready
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.