AI

AI Engineer Master Program

Comprehensive AI Engineer program covering Programming & System Foundations, Mathematics for AI, Core ML, Deep Learning, Generative AI & LLM Engineering, Production AI Engineering, Security & Responsible AI, Advanced Topics (RL, Edge AI, System Design, Research), and Industry Projects (End-to-End …

3 Months Duration
32 Modules
Paid Access

Course fee ₹5000.00

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

Advanced Python for AI

OOP & design patterns, functional programming, generators & iterators, async, type hinting, memory management, profiling, packaging & dependencies.

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Learning objectives

Apply OOP and design patterns
Use functional programming, generators, iterators
Apply async programming and type hinting
Manage memory and optimize with profiling
Handle packaging and dependency management

Module 2 Locked

Data Structures & Algorithms for AI Engineers

Arrays, strings, linked lists, stacks, queues, trees, graphs, hashing, recursion, backtracking, sorting, searching, time & space complexity.

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Learning objectives

Implement arrays, strings, linked lists, stacks, queues
Work with trees and graphs
Apply hashing, recursion, backtracking
Use sorting and searching
Analyze time and space complexity

Module 3 Locked

Software Engineering for AI

Clean code, SOLID, API design, unit/integration testing, logging & monitoring, Git advanced, CI/CD pipelines.

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Learning objectives

Apply clean code and SOLID principles
Design APIs and write unit/integration tests
Use logging and monitoring
Apply Git advanced and CI/CD pipelines

Module 4 Locked

Linear Algebra

Vector spaces, matrix operations, eigenvalues & eigenvectors, SVD, norms, linear transformations.

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Learning objectives

Work with vector spaces and matrix operations
Apply eigenvalues, eigenvectors, SVD
Use norms and linear transformations

Module 5 Locked

Calculus & Optimization

Derivatives, partial derivatives, chain rule, gradients, Jacobian & Hessian, optimization algorithms.

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Learning objectives

Apply derivatives, partial derivatives, chain rule
Use gradients, Jacobian, Hessian
Apply optimization algorithms

Module 6 Locked

Probability & Statistics

Random variables, distributions, Bayesian inference, maximum likelihood, hypothesis testing, A/B testing, statistical significance.

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Learning objectives

Work with random variables and distributions
Apply Bayesian inference and maximum likelihood
Use hypothesis testing, A/B testing, statistical significance

Module 7 Locked

ML Fundamentals

ML pipeline, bias-variance, overfitting & regularization, evaluation metrics, feature engineering.

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Learning objectives

Build ML pipeline and understand bias-variance
Address overfitting and regularization
Use evaluation metrics and feature engineering

Module 8 Locked

Supervised Learning

Linear models, tree-based models, ensemble methods, gradient boosting, model interpretation.

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Learning objectives

Apply linear and tree-based models
Use ensemble methods and gradient boosting
Interpret models

Module 9 Locked

Unsupervised Learning

Clustering, dimensionality reduction, anomaly detection, topic modeling.

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Learning objectives

Apply clustering and dimensionality reduction
Detect anomalies and perform topic modeling

Module 10 Locked

Advanced ML

Imbalanced data, multi-label classification, time series forecasting, recommender systems, graph ML.

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Learning objectives

Handle imbalanced data and multi-label classification
Build time series and recommender systems
Apply graph ML

Module 11 Locked

Neural Networks

Perceptron, activation functions, backpropagation, optimization strategies, regularization.

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Learning objectives

Understand perceptron and activation functions
Apply backpropagation and optimization strategies
Use regularization

Module 12 Locked

CNN & Computer Vision

CNN architecture, object detection, segmentation, transfer learning, vision transformers.

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Learning objectives

Design CNN and perform object detection, segmentation
Apply transfer learning and vision transformers

Module 13 Locked

NLP & Sequence Models

Text preprocessing, RNN, LSTM, GRU, attention, Transformers.

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Learning objectives

Preprocess text and use RNN, LSTM, GRU
Apply attention and Transformers

Module 14 Locked

Multimodal AI

Vision + text, audio + text, image captioning, CLIP models.

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Learning objectives

Build vision+text and audio+text models
Apply image captioning and CLIP

Module 15 Locked

Transformer Architecture Deep Dive

Self attention, multi-head attention, encoder-decoder, scaling laws.

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Learning objectives

Understand self attention and multi-head attention
Apply encoder-decoder and scaling laws

Module 16 Locked

Large Language Models

GPT architecture, pretraining, fine-tuning, instruction tuning, LoRA & PEFT.

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Learning objectives

Understand GPT and pretraining
Apply fine-tuning, instruction tuning, LoRA & PEFT

Module 17 Locked

Prompt Engineering

Prompt patterns, few-shot prompting, chain of thought, system prompts, guardrails.

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Learning objectives

Apply prompt patterns and few-shot prompting
Use chain of thought and system prompts
Implement guardrails

Module 18 Locked

Retrieval Augmented Generation (RAG)

Embeddings, vector databases, chunking strategies, hybrid search, reranking.

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Learning objectives

Use embeddings and vector databases
Apply chunking, hybrid search, reranking

Module 19 Locked

AI Agents & Tool Calling

Agent architectures, tool integration, function calling, multi-agent systems, workflow automation.

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Learning objectives

Design agent architectures and integrate tools
Use function calling and multi-agent systems
Automate workflows

Module 20 Locked

Model Deployment

FastAPI, gRPC, Docker, Kubernetes, model versioning.

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Learning objectives

Deploy with FastAPI, gRPC, Docker, Kubernetes
Apply model versioning

Module 21 Locked

MLOps

MLflow, DVC, feature stores, monitoring, drift detection.

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Learning objectives

Use MLflow, DVC, feature stores
Apply monitoring and drift detection

Module 22 Locked

Distributed AI Systems

Distributed training, data parallelism, model parallelism, GPU optimization, inference scaling.

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Learning objectives

Apply distributed training and data/model parallelism
Optimize GPU and scale inference

Module 23 Locked

Cloud AI Engineering

AWS, GCP, Azure, serverless AI, auto-scaling.

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Learning objectives

Use AWS, GCP, Azure for AI
Apply serverless AI and auto-scaling

Module 24 Locked

Responsible AI

Bias & fairness, explainability, ethical AI, governance.

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Learning objectives

Address bias, fairness, explainability
Apply ethical AI and governance

Module 25 Locked

AI Security

Prompt injection, model attacks, data poisoning, secure deployment.

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Learning objectives

Defend against prompt injection and model attacks
Address data poisoning and secure deployment

Module 26 Locked

Reinforcement Learning

MDP, Q-Learning, policy gradient, deep RL.

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Learning objectives

Model with MDP and apply Q-Learning
Use policy gradient and deep RL

Module 27 Locked

Edge AI

Model quantization, ONNX, TensorRT, mobile AI.

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Learning objectives

Apply quantization, ONNX, TensorRT
Deploy mobile AI

Module 28 Locked

AI System Design

End-to-end architecture, real-time AI, feature stores, high availability.

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Learning objectives

Design end-to-end and real-time AI systems
Use feature stores and high availability

Module 29 Locked

AI Research Foundations

Reading papers, implementing papers, experiment design, benchmarking.

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Learning objectives

Read and implement research papers
Design experiments and run benchmarks

Module 30 Locked

End-to-End ML System

Data → Model → Deployment → Monitoring.

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Learning objectives

Build full pipeline from data to deployment and monitoring

Module 31 Locked

LLM Production App

RAG chatbot, authentication, monitoring, scaling.

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Learning objectives

Build RAG chatbot with authentication, monitoring, scaling

Module 32 Locked

AI SaaS Architecture

Multi-tenant AI, billing integration, logging & observability.

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Learning objectives

Design multi-tenant AI with billing and observability

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