MODULE 1: Foundations of AI & Python for AI

• AI vs ML vs DL

• Python fundamentals for AI

•  Numpy, Pandas, Jupyter Notebook

​

  MODULE 2: Mathematics for AI

•  Linear Algebra basics

•  Calculus (gradients)

•  Probability & statistics

• Model evaluation metrics

​

  MODULE 3: Machine Learning Essentials

•   Regression, Classification

•   SVM, Decision Trees, Random Forest

•   Clustering (K-Means)

•   Cross-validation & tuning

​

  MODULE 4: Deep Learning & Neural Networks

• Neural networks architecture

• Training & optimization

• CNN fundamental

• TensorFlow & PyTorch intro

​

  MODULE 5: NLP & LLM Engineering

• NLP basics

• Tokenization, embeddings

• Transformers (BERT, GPT)

• Fine-tuning & RAG

• HuggingFace usage

​

  MODULE 6: Data Engineering for AI

• Data pipelines

• ETL & preprocessing

• Pandas for data cleaning

​

  MODULE 7: Model Deployment & MLOps

• Flask/FastAPI deployment

• Docker basics

• CI/CD overview

• Cloud ML deployment

​

  MODULE 8: GenAI Engineering & Agents

• OpenAI API usage

• Function calling

• Vector DB (FAISS)

• LangChain

​

  MODULE 9: AI Ethics & Governance

• Responsible AI practices

​

  MODULE 10: Deploying AI Model - MLOps

•  Basic understanding of Cloud services

•  Kubernetes and Docker

• Model Deployment and Monitoring

​

  MODULE 11: Career & Portfolio Training

• ATS Resume

• GitHub portfolio

• Mock interviews

• Projects

​

​

​

​

​

​