Natural Language Processing (NLP)
  • NLP Basics
  • Text preprocessing: Tokenization, Lemmatization, Stopwords
  • BoW, TF-IDF, Word2Vec
  • Text Classification, Sentiment Analysis
  • Project 5: Sentiment Analysis of Product Reviews

Deep Learning
  • Artificial Neural Networks (ANN)
    • Perceptron, Multilayer Perceptron
    • Forward & Backward Propagation
    • Activation, Loss functions, Optimizers
    • Overfitting, Regularization, Batch Normalization
    • Project 6: Iris Flower Classification using ANN
  • Computer Vision (CNNs for Image Processing)
    • Convolution, Pooling, Padding
    • Transfer Learning (VGG, ResNet)
    • R-CNN, Faster R-CNN
    • Project 7: Handwritten Digit Recognition
  • RNNs & LSTMs
    • RNN, LSTM, GRU, Bidirectional RNN
    • Sequence Modeling, Time Series, Text Generation
    • Project 8: Next Word Prediction using RNN & LSTM

(Add on) : Transformers & BERT
  • Attention Mechanism, Transformer Architecture
  • Using Pre-trained Models (BERT, RoBERTa)
  • Embedding generation & fine-tuning
  • Project 9: Sentiment Analysis using BERT (Transformers)

(Add on) : Generative AI, LLMs (Large Language Models) & OpenAI
  • GenAI
    • What is GenAI? Prompt Engineering
    • Use cases: Q&A, Summarization, Image Generation
    • Ethical concerns & safety
  • LLM Apps with OpenAI & LangChain
    • Using ollama
    • LangChain Pipelines, FAISS, ChromaDB
    • Chat with PDF, CSV, Excel
    • Project 10: Flow Stack Machine Learning Project

(Add on) : Deployment & MLOps
  • Web Deployment: Streamlit or Flask
  • Model Deployment on AWS with CI/CD & Monitoring
  • Creating AWS instance
  • Dockerizing models, CI/CD with GitHub Actions
  • Model Tracking with MLflow
  • Note - this module will be mostly online as trainers aare rare.