📖 Weekly Study Guides
Week 1 · Jan 12–16
Text Preprocessing & Text Representations
Modules 1 & 2 — Foundational NLP
- Tokenization, Stemming, Lemmatization
- Stop words, N-grams, Noise removal
- One-Hot Encoding (sparse vectors)
- Bag of Words (frequency counts)
- TF-IDF (importance weighting)
Week 2 · Jan 19–23
Naive Bayes & Classification Evaluation
Module 3 — First Classifier
- Bayes' Rule: Prior, Likelihood, Posterior
- Generative vs Discriminative models
- Multinomial Naive Bayes for NLP
- Confusion Matrix, Accuracy, Precision, Recall
- ROC Curve & AUC
Week 3 · Jan 26–30
Logistic Regression, SVM & Perceptron
Module 4 — Linear Classifiers
- Sigmoid function & soft classification
- Gradient descent & MLE
- Support Vector Machines & margin
- Kernel trick (non-linear SVM)
- Perceptron algorithm & convergence
Week 4 · Feb 2–6
SVD, Co-occurrence & GloVe
Module 5 — Word Embeddings I
- Why dimensionality reduction?
- Co-occurrence matrix construction
- Singular Value Decomposition (SVD)
- Dense word embeddings from SVD
- GloVe: global co-occurrence + semantics
Week 5 · Feb 9–13
Neural Networks & Word2Vec
Module 6 — Deep Learning Intro
- Activation functions: ReLU, Sigmoid, Tanh
- Forward pass & Backpropagation
- Stochastic & Batch Gradient Descent
- Word2Vec: CBoW model
- Word2Vec: Skip-Gram model
Week 6 · Feb 16–20
Convolutional & Recurrent Neural Networks
Module 7 — Sequence & Structure
- CNN: filters, convolution, feature maps
- Max pooling & parameter sharing
- CNN for text classification
- RNN: sequential memory & hidden state
- Vanishing gradient & exploding gradient
Week 7 · Feb 23–Mar 1 ← YOU ARE HERE
LSTM, GRU & Attention
Module 8 — Long-Term Memory & Focus
- LSTM: cell state, forget / input / output gates
- Cell state update: C_t = f_t⊙C_{t-1} + i_t⊙C̃_t
- GRU: 2-gate simplified LSTM
- Encoder-Decoder architecture
- Additive Attention (Bahdanau): α weights + C_t
🗺️ Course Learning Roadmap
From raw text → intelligent models
Raw Text
→
Preprocessing
W1
W1
→
Representations
W1
W1
→
Classifiers
W2–W3
W2–W3
→
Embeddings
W4–W5
W4–W5
→
Deep Models
W5–W6
W5–W6
→
Transformers
W9
W9
→
LLMs / Agents
W13–W14
W13–W14
💡 How to Use These Notes
What → Why → How
Every concept is explained in three layers: what it is, why we need it, and how it works mechanically. Read all three before moving on.
Use the Interactive Demos
Each week has live calculators and visual sliders. Change the inputs and watch outputs update — this is the fastest way to build intuition.
Quiz Yourself
Each week ends with quiz-style questions modeled after actual Quiz 1–4. Cover the answer, try it yourself, then reveal. Repeat weak spots.
Follow the Thread
Each week builds on the last. If something is confusing in Week 5, go back to Week 3. The roadmap above shows exactly how concepts connect.