AI Engineering Curriculum

A comprehensive roadmap designed to take you from writing your first Python script to deploying autonomous AI agents.

Phase 1: Python Foundations

Master the language of AI. From syntax to advanced functional programming.

Variables, Data Types, and Control Flow
Functions, Modules, and Packages
Object-Oriented Programming (OOP)
File Handling & Error Management
AsyncIO and Concurrency

Phase 2: Data Structures & Algorithms

Build the problem-solving skills necessary for efficient code.

Arrays, Linked Lists, Stacks, Queues
Trees, Graphs, and Heaps
Sorting and Searching Algorithms
Dynamic Programming
Time & Space Complexity Analysis

Phase 3: Machine Learning Basics

Understand the core algorithms that drive intelligent systems.

Supervised vs Unsupervised Learning
Linear/Logistic Regression
Decision Trees & Random Forests
Support Vector Machines (SVM)
Model Evaluation Metrics

Phase 4: Deep Learning & Neural Networks

Dive deep into neural architectures with PyTorch.

Perceptrons & Multi-Layer Perceptrons
Backpropagation & Gradient Descent
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) & LSTMs
Transfer Learning

Phase 5: NLP & Transformers

Teach machines to understand and generate human language.

Tokenization & Embeddings (Word2Vec, GloVe)
Attention Mechanisms
Transformer Architecture (BERT, GPT)
HuggingFace Transformers Library
Fine-tuning Pre-trained Models

Phase 6: GenAI & LLM Agents

Build the future with Large Language Models and Autonomous Agents.

Prompt Engineering Strategies
Retrieval Augmented Generation (RAG)
Vector Databases (Pinecone, Weaviate)
LangChain & LlamaIndex
Building Autonomous Agents

Phase 7: MLOps & Deployment

Take your models from notebook to production.

Model Serving (FastAPI, Ray Serve)
Containerization (Docker, Kubernetes)
CI/CD for ML Pipelines
Model Monitoring & Observability
Cloud Deployment (AWS/GCP)

Ready to start your journey?

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