Certification in AI & ML - AI Engineer's Toolkit
Course Duration
8 months
Time Commitment
10 Hours/Week
Eligibility
12th Pass and Above
Learning Mode
Online
4.75K+ students have already registered
Course Duration
8 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?
A rigorous, foundations-first program. Master classical ML deeply, build GenAI awareness, and graduate as an engineer who knows why things work — not just how to run them.
Toolkit
Course Details
Duration
8 Months
Course Mode
Online
Certification
from Vishlesan i-Hub IIT Patna
- • Build Python the right way — OOP, modular code, APIs, environment setup, and proper structuring from day one
- • Go deep on NumPy and pandas: indexing, joins, reshaping, groupby, missing value strategies, and performance habits
- • Perform rigorous EDA — univariate and bivariate analysis, correlation mapping, feature relationships, and leakage detection
- • Build solid mathematical intuition for ML: linear algebra, probability, distributions, derivatives, and gradient descent
- • Work like an engineer from the start — Git, GitHub, clean notebooks, reproducible folder structures, and environment discipline
- • Master every major ML family — regression, classification, clustering, tree-based models with full assumption coverage
- • Understand overfitting and underfitting deeply: bias-variance tradeoff, regularisation, and how to build strong baselines before iterating
- • Evaluate models rigorously — precision, recall, AUC, confusion matrices, and knowing which metric to trust for which problem
- • Build end-to-end feature pipelines with cross-validation, hyperparameter tuning, experiment tracking, and model interpretation via SHAP
- • Practice reproducible, well-documented ML workflows that hold up beyond the notebook and can be handed to another engineer
- • Understand neural networks deeply — activation functions, loss surfaces, backpropagation, and optimiser behaviour
- • Build CNN and RNN models, understand where they work and where they break, and apply transfer learning for fast results
- • Work across text and image modalities — preprocessing, embeddings, and classification pipelines for both
- • Know the boundary between classical ML and deep learning: when the added complexity pays off and when it doesn't
- • Understand transformer architecture in depth — self-attention, positional encoding, pretraining, RLHF, and inference behaviour
- • Design robust LLM application flows: prompt architecture, system prompts, structured outputs, function calling, and tool orchestration
- • Build production-grade RAG systems with chunking strategies, hybrid retrieval, reranking, query rewriting, and failure analysis
- • Know when to prompt, when to use RAG, and when fine-tuning is worth the cost — with evaluation frameworks to validate the call
- • Develop awareness of agents: tools, planning, memory, loops, and simple multi-step task execution as a practitioner concept
- • Serve ML models as live APIs with FastAPI — serialisation, endpoints, logging, and basic cloud deployment via Docker
- • Understand production challenges in GenAI: hallucination, cost, latency, prompt drift, model updates, and guardrail design
- • Apply version control and engineering workflow to every project — branches, PRs, environment files, and reproducibility standards
- • Build a portfolio of projects that are documented, versioned, and deployable — not just notebooks that run once
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.