Last Updated on July 22, 2025

✅ TRACK 1: General AI Engineering — Zero to Agentic AI (Full Stack to Architect)

🧠 For all developers, engineers, and architects building AI/LLM-native solutions

🌱 Foundations & Programming

Goal: Build a strong base in programming and AI tools.

  • 🔹 Python for AI: NumPy, Pandas, Matplotlib
  • 🔹 Machine Learning Basics:
    • Supervised Learning: Sklearn, Linear/Logistic Regression
    • Ensemble Methods: XGBoost, Random Forest
    • Unsupervised Learning: Clustering, PCA
  • 🔹 Deep Learning Essentials:
    • Artificial Neural Networks (ANN)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN, LSTM)

💬 NLP & GenAI Engineering

Goal: Master language models, chat interfaces, and modern GenAI tools.

  • 🔹 Prompt Engineering: GPT, Claude, Gemini, best practices
  • 🔹 LLM Integration Frameworks: LangChain, LlamaIndex
  • 🔹 Chatbots & RAG Systems:
    • Retrieval-Augmented Generation (Vector DBs, Pinecone, FAISS)
    • Context Management & Multi-turn Dialogue
  • 🔹 HuggingFace & NLP Apps: Transformers, Tokenizers, Named Entity Recognition, Sentiment Analysis

👁️ Computer Vision

Goal: Develop applications using image and video data.

  • 🔹 Image Tasks: Classification, Detection (YOLO, SSD)
  • 🔹 OCR Solutions: Tesseract, OpenCV
  • 🔹 Document & Visual Intelligence: Layout Parsing, Form Extraction, Visual Question Answering (VQA)

🤖 AI Agents & Agentic Frameworks

Goal: Move toward autonomous, multi-step intelligent systems.

  • 🔹 Core Agentic Frameworks:
    • ReAct, BabyAGI, CrewAI, AutoGPT
  • 🔹 Multi-Agent Systems: Task Delegation, Planning, Coordination
  • 🔹 Advanced Capabilities:
    • Tool Calling, Memory Integration (VectorDB + Long-Term)
    • Feedback Loops, Dynamic Reasoning, Chain-of-Thought Execution

🧰 MLOps & Production AI

Goal: Deploy, scale, and manage AI systems reliably.

  • 🔹 Experiment Tracking & Pipelines: MLflow, DVC, Kubeflow
  • 🔹 Model Lifecycle Management: Model Registry, Versioning
  • 🔹 CI/CD & Deployment: Docker, FastAPI, GPU Scaling (AWS/GCP/Azure)

🔐 Secure & Responsible AI

Goal: Build ethical, secure, and compliant AI applications.

Responsible Model Usage Policies

🔹 AI Ethics & Governance: Fairness, Bias Auditing

🔹 Security in AI:

Prompt Injection Protection

Adversarial Examples & Robust Training

🔹 Compliance & Privacy:

GDPR, DPDP Act (India), Consent-based Models