AI Engineering

Complete AI Engineering Curriculum

A structured 8-phase journey from Python fundamentals to advanced Agentic AI systems. Build a production-ready portfolio with 20+ real-world projects.

01Foundations
02Data Science
03Machine Learning
04Deep Learning
05Computer Vision
06NLP & LLMs
07AI Agents
08Production AI
01

Foundations

Python programming, OOP, and AI fundamentals

Module 01

Introduction to Artificial Intelligence

AI Fundamentals

  • What is Artificial Intelligence
  • History and Evolution of AI
  • Types of AI: Narrow, General, Super
  • AI vs Machine Learning vs Deep Learning
  • Intelligence: Human vs Machine

AI Applications & Ethics

  • AI Applications in Industry
  • Ethics and Responsible AI
  • Bias and Fairness in AI
  • Future Trends in AI
Module 02

Python & Object-Oriented Programming

Python Essentials

  • Environment Setup & IDEs
  • Variables and Data Types
  • Strings, Lists, Tuples, Sets, Dictionaries
  • Control Flow and Loops
  • Functions and Lambda Expressions
  • File Handling and Exception Management

Object-Oriented Programming

  • Classes and Objects
  • Inheritance and Polymorphism
  • Encapsulation and Abstraction
  • Decorators and Generators
  • Modules and Packages
02

Data Science

Data manipulation, visualization, and preprocessing

Module 03

Exploratory Data Analysis with NumPy & Pandas

NumPy for Numerical Computing

  • Arrays and Matrix Operations
  • Slicing, Indexing, and Reshaping
  • Broadcasting and Vectorization
  • Statistical Functions
  • Universal Array Functions

Pandas for Data Manipulation

  • Series and DataFrames
  • Data Loading and Exporting
  • Handling Missing Data
  • Grouping and Aggregation
  • Merging, Joining, and Concatenating
  • Pivot Tables
Module 04

Data Visualization & Dashboarding

Visualization Libraries

  • Matplotlib for Basic Plotting
  • Seaborn for Statistical Visualization
  • Plotly for Interactive Charts
  • Customizing Visuals for Storytelling

Dashboard Design

  • Design Principles for Data Dashboards
  • Building Interactive Dashboards
Module 05

Data Preprocessing & ETL Pipelines

Data Preparation

  • Handling Duplicates and Outliers
  • Feature Engineering and Selection
  • Data Scaling and Normalization
  • Cross-Validation Strategies

Data Pipelines

  • Building Robust ETL Pipelines
  • Automating Data Workflows
03

Machine Learning

Fundamental algorithms and techniques for predictive modeling

Module 06

Machine Learning Fundamentals

Supervised Learning

  • Linear and Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Naive Bayes Classifier
  • Model Evaluation Metrics

Unsupervised Learning

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)

Advanced ML Techniques

  • Hyperparameter Tuning
  • Ensemble Methods
  • Cross-Validation and Bootstrapping
  • Bias-Variance Tradeoff
04

Deep Learning

Neural networks, loss functions, and optimization

Module 07

Deep Learning & Neural Networks

Neural Network Architecture

  • Artificial Neural Networks (ANN)
  • Activation Functions
  • Backpropagation Algorithm
  • Optimization Algorithms
  • Regularization Techniques

Deep Learning Frameworks

  • TensorFlow Fundamentals
  • PyTorch Fundamentals
  • Keras for Rapid Prototyping
05

Computer Vision

Image processing, object detection, and vision models

Module 08

Computer Vision Architectures

CV Architectures & Techniques

  • Convolutional Neural Networks (CNNs)
  • Image Classification Models
  • Object Detection (YOLO, Faster R-CNN)
  • Transfer Learning and Fine-Tuning
  • Image Segmentation
  • Evaluation Metrics: mAP, IoU

Practical Computer Vision

  • OpenCV for Image Processing
  • Real-Time Object Detection
  • Face Recognition Systems
06

NLP & Large Language Models

Transformers, prompt engineering, and RAG systems

Module 09

Natural Language Processing

Text Processing & Embeddings

  • Tokenization and Text Cleaning
  • Word Embeddings (Word2Vec, GloVe)
  • Contextual Embeddings (BERT, GPT)

Sequence Models & Transformers

  • RNN, LSTM, and GRU Architectures
  • Attention Mechanisms
  • Transformer Architecture
  • Multi-Head Attention

Modern NLP

  • Hugging Face Transformers Library
  • Text Classification and Summarization
  • Sentiment Analysis
Module 10

Large Language Models & RAG Systems

LLM Engineering

  • Prompt Engineering Strategies
  • LLM APIs Integration (OpenAI, Groq)
  • Fine-Tuning and Parameter Efficient Tuning
  • Understanding Tokens and Context Windows
  • Handling Hallucinations and Knowledge Cutoff

RAG Systems

  • Vector Databases (ChromaDB, FAISS, Pinecone)
  • Document Chunking and Embedding
  • Retrieval Strategies (Cosine Similarity, Hybrid Search)
  • Building End-to-End RAG Pipelines
  • Advanced RAG: Multi-Query and Re-Ranking
07

AI Agents

Autonomous systems, multi-agent workflows, and orchestration

Module 11

AI Agents & Multi-Agent Systems

Agent Design & Architecture

  • Agent Architecture and Design Patterns
  • Tool Use and Function Calling
  • Memory Systems (Short-term, Long-term, Episodic)
  • LangChain Framework for Agents
  • ReAct Pattern (Reasoning + Acting)

Multi-Agent Orchestration

  • Multi-Agent Workflows
  • LangGraph for Complex Orchestration
  • Building Autonomous Research Agents
  • Conditional Flows and Human-in-the-Loop
08

Production AI

Deploy, scale, and monitor AI applications in production

Module 12

Capstone & Production Deployment

Production AI

  • FastAPI for AI Services
  • Model Deployment and Serving
  • Cloud Integration (AWS, GCP, Azure)
  • Docker and Containerization

Capstone Project

  • End-to-End AI System Development
  • Portfolio Building and Documentation
  • Industry Best Practices
  • Interview Preparation and Career Support

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