Data Handling & Exploratory Analysis

• Data Cleaning and Preparation
• Handling Missing Values and Outliers
• Feature Scaling and Encoding
• Exploratory Data Analysis (EDA)

Machine Learning (ML)

• Introduction to Machine Learning
• Supervised vs Unsupervised Learning
• ML lifecycle & workflow
• Model building intuition (how models actually learn)

Regression

• Regression Algorithms
• Regression evaluation:
• MSE, RMSE, R² with hands-on examples

Classification

• Classification Algorithms
• Classification evaluation:
• Confusion Matrix, Precision, Recall, F1-score
• Feature selection techniques
• Overfitting vs underfitting (with examples)
• Model Evaluation Metrics
• Cross-Validation and Hyperparameter Tuning

Unsupervised Learning

• Clustering Algorithms
• K-Means clustering with Elbow Method
• Distance metrics (Euclidean, Manhattan – basic)
• Real-world clustering use cases
• Mini project on clustering

Advanced Machine Learning

• Decision Trees and Random Forest
• Ensemble Learning Techniques
• Support Vector Machines (SVM)
• Dimensionality Reduction (PCA, LDA)

Natural Language Processing (NLP)

• NLP pipeline (data → model → evaluation)
• Text Preprocessing Techniques
• Text cleaning with real datasets
• Tokenization, stemming vs lemmatization
• Feature Extraction (BoW, TF-IDF)
• Word Embeddings
• Word clouds
• Text Classification
• Sentiment Analysis
• End-to-end sentiment analysis project
• NLP mini project before capstone

Deep Learning

• Introduction to Neural Networks
• Perceptron model
• Activation and Loss Functions
• Forward & backward propagation intuition
• Backpropagation and Optimization
• ANN build using Keras (step-by-step)
• Epochs, batches, optimizers in practice
• Performance improvement case study
• Recurrent Neural Networks (RNN, LSTM, GRU)

Computer Vision

• Convolutional Neural Networks (CNN)
• CNN architecture breakdown
• MNIST / Image classification project
• Activation maps & feature extraction
• Transfer learning mini project

Advanced NLP & Transformers

• Sequence Models
• Attention Mechanism
• Transformers and BERT
• Hugging Face setup & usage
• Transformer fine-tuning steps
• Text classification using BERT
• Text Summarization and Language Modeling
• Evaluation best practices for transformers

Generative AI & Advanced AI

• Generative AI Fundamentals
• Large Language Models (LLMs)
• Prompt Engineering

Ethical and Responsible AI Real-World AI Use Cases

Capstone Project

• Problem Definition and Data Understanding
• End-to-End Model Development
• Model Evaluation and Interpretation
• Final Presentation and Documentation