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AI Engineering

AI Engineering focuses on building, deploying, and scaling production-ready AI systems.

Tools you’ll work with
PyTorch
Hugging Face
OpenAI API
LangChain
Pinecone
Docker
+2 more tools
₹38000
12 weeksBeginner
Instructor
Arun S
MSc AI • 10+ years • Ex - Microsoft
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Course Overview

Description

25 lessons•57 exercises•4 exams•~12 hours

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you.

If terms like traditional data, big data, business intelligence, and machine learning sound confusing, this course will help you understand both meaning and practical application.

25 lessons•57 exercises•4 exams•12 hours

Course Curriculum

A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.

This section introduces key concepts and builds intuition through structured lessons and exercises.

Neural Network architectures: Perceptrons to Multi-layer Perceptrons
Backpropagation, Loss functions, and Optimization (Adam, SGD)
PyTorch fundamentals: Tensors, Autograd, and Module building
Convolutional Neural Networks (CNNs) for Computer Vision
Sequence modeling with RNNs and LSTMs

This section introduces key concepts and builds intuition through structured lessons and exercises.

The Attention Mechanism: Self-attention and Multi-head attention
Transformer architecture: Encoders (BERT) and Decoders (GPT)
Tokenization, Embeddings, and Context Windows
Prompt Engineering: Zero-shot, Few-shot, and Chain-of-Thought
Working with Hugging Face Transformers library

This section introduces key concepts and builds intuition through structured lessons and exercises.

Building Retrieval-Augmented Generation (RAG) systems
Vector Databases: Pinecone, ChromaDB, and Weaviate
Chunking strategies and Semantic Search
Fine-tuning LLMs: SFT, PEFT, and LoRA/QLoRA
Evaluation frameworks for RAG (RAGAS) and LLM outputs
Forecast evaluation (MAPE, RMSE)

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to AI Agents and Autonomous Workflows
Tool calling and Function execution with LLMs
Multi-agent systems using LangGraph and CrewAI
Memory management in conversational AI
Building custom GPTs and AI-powered assistants

This section introduces key concepts and builds intuition through structured lessons and exercises.

API development for AI using FastAPI
Containerization with Docker and Orchestration with Kubernetes
Model serving with NVIDIA Triton or vLLM
Monitoring LLM latency, cost, and hallucinations
CI/CD for AI: Automated testing and model versioning

How You’ll Learn

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Live Classes
Quizzes
Assignments
Projects
Certification

Build Skills That Scale With You

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Learn deeply. Apply repeatedly.