BSDA5005 · workspace

Nlp

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Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction to Natural Language (NL) Why is it hard to process a natural language? Levels of Language Processing, Linguistic Fundamentals for NLP

Incomplete

02W02

Text Processing and Preprocessing: Tokenization, Normalization, Stop word removal, Stemming, lemmatization, Morphological Analysis & Finite State Tran

Incomplete

03W03

Part-of-speech tagging and Named Entities; Sequence Models: Hidden-Markov Models, MEMM and CRF; Classification Models: Naïve Bayes, Logistic Regress

Incomplete

04W04

Syntax and Parsing: Constituency parsing, Dependency parsing, Parsing algorithms; Meaning Representation: Logical Semantics, Semantic Role Labelling

Incomplete

05W05

Distributional Semantics, n-gram and Word2Vec, GloVe; Discourse Processing: Anaphora and Coreference Resolution and Discourse Connectives. Machine Tr

Incomplete

06W06

Recurrent neural networks, LSTMs/GRUs, Neural Sequence Models, Contextualized Word Embeddings: TagLM, ELMO, ULMFIT, etc., Attention Mechanism (Code de

Incomplete

07W07

Transformers, Self-attention Mechanism, Sub-word tokenization, Positional encoding, Pre-trained Language Models (PLMs): BERT, GPT, etc. Fine-tuning an

Incomplete

08W08

Large Language Models (LLMs) Parameter Efficient Fine Tuning: Prefix-coding, LORA, QLORA, etc. Emergent Behavior: In-context learning, Instruction Fin

Incomplete

09W09

Natural Language Generation, Decoding schemes: greedy, Random sampling, Top-k, Top-p, Speculative sampling, etc.

Incomplete

010W10

NLP applications: QnA, Summarization, NLI, Fact-checking, etc. Retrieval Augmented generation (RAG). (Code demo: Summarisation)

Incomplete

011W11

Model Explainability: Attention maps, Attention-flow/rollout, Integrated gradients, etc. (Code demo: Attension visualization and Integrated gradients

Incomplete

012W12

Ethical and cultural considerations and biases.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

2 min

BSDA5005 — Natural Language Processing (NLP)

Open this first for the core logic and traps.

Dive in

3 min

Named Entity Recognition with Sequence Labeling

Open this first for the core logic and traps.

Dive in

4 min

NLP Introduction & Text Preprocessing

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSDA5005

PG / MTech

4 Credits

Introduction to Natural Language Processing (i-NLP)

Natural language (NL) refers to the language spoken/written by humans. NL is the primary mode of communication for humans. With the growth of the w...

Required Dependencies:

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction to Natural Language (NL) Why is it hard to process a natural language? Levels of Language Processing, Linguistic Fundamentals for NLP

Module 2

Text Processing and Preprocessing: Tokenization, Normalization, Stop word removal, Stemming, lemmatization, Morphological Analysis & Finite State Tran

Module 3

Part-of-speech tagging and Named Entities; Sequence Models: Hidden-Markov Models, MEMM and CRF; Classification Models: Naïve Bayes, Logistic Regress

Module 4

Syntax and Parsing: Constituency parsing, Dependency parsing, Parsing algorithms; Meaning Representation: Logical Semantics, Semantic Role Labelling

Module 5

Distributional Semantics, n-gram and Word2Vec, GloVe; Discourse Processing: Anaphora and Coreference Resolution and Discourse Connectives. Machine Tr

Module 6

Recurrent neural networks, LSTMs/GRUs, Neural Sequence Models, Contextualized Word Embeddings: TagLM, ELMO, ULMFIT, etc., Attention Mechanism (Code de

Module 7

Transformers, Self-attention Mechanism, Sub-word tokenization, Positional encoding, Pre-trained Language Models (PLMs): BERT, GPT, etc. Fine-tuning an

Module 8

Large Language Models (LLMs) Parameter Efficient Fine Tuning: Prefix-coding, LORA, QLORA, etc. Emergent Behavior: In-context learning, Instruction Fin

Module 9

Natural Language Generation, Decoding schemes: greedy, Random sampling, Top-k, Top-p, Speculative sampling, etc.

Module 10

NLP applications: QnA, Summarization, NLI, Fact-checking, etc. Retrieval Augmented generation (RAG). (Code demo: Summarisation)

Module 11

Model Explainability: Attention maps, Attention-flow/rollout, Integrated gradients, etc. (Code demo: Attension visualization and Integrated gradients

Module 12

Syllabus

Topic

Introduction to Natural Language (NL) Why is it hard to process a natural language? Levels of Language Processing, Linguistic Fundamentals for NLP

Text Processing and Preprocessing: Tokenization, Normalization, Stop word removal, Stemming, lemmatization, Morphological Analysis & Finite State Tran

Part-of-speech tagging and Named Entities; Sequence Models: Hidden-Markov Models, MEMM and CRF; Classification Models: Naïve Bayes, Logistic Regress

Syntax and Parsing: Constituency parsing, Dependency parsing, Parsing algorithms; Meaning Representation: Logical Semantics, Semantic Role Labelling

Distributional Semantics, n-gram and Word2Vec, GloVe; Discourse Processing: Anaphora and Coreference Resolution and Discourse Connectives. Machine Tr

Recurrent neural networks, LSTMs/GRUs, Neural Sequence Models, Contextualized Word Embeddings: TagLM, ELMO, ULMFIT, etc., Attention Mechanism (Code de

Transformers, Self-attention Mechanism, Sub-word tokenization, Positional encoding, Pre-trained Language Models (PLMs): BERT, GPT, etc. Fine-tuning an

Large Language Models (LLMs) Parameter Efficient Fine Tuning: Prefix-coding, LORA, QLORA, etc. Emergent Behavior: In-context learning, Instruction Fin

Natural Language Generation, Decoding schemes: greedy, Random sampling, Top-k, Top-p, Speculative sampling, etc.

NLP applications: QnA, Summarization, NLI, Fact-checking, etc. Retrieval Augmented generation (RAG). (Code demo: Summarisation)

Model Explainability: Attention maps, Attention-flow/rollout, Integrated gradients, etc. (Code demo: Attension visualization and Integrated gradients

Ethical and cultural considerations and biases.

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Introduction to Natural Language Processing (i-NLP)

Execution Protocol

Topic

Introduction to Natural Language (NL) Why is it hard to process a natural language? Levels of Language Processing, Linguistic Fundamentals for NLP

Text Processing and Preprocessing: Tokenization, Normalization, Stop word removal, Stemming, lemmatization, Morphological Analysis & Finite State Tran

Part-of-speech tagging and Named Entities; Sequence Models: Hidden-Markov Models, MEMM and CRF; Classification Models: Naïve Bayes, Logistic Regress

Syntax and Parsing: Constituency parsing, Dependency parsing, Parsing algorithms; Meaning Representation: Logical Semantics, Semantic Role Labelling

Distributional Semantics, n-gram and Word2Vec, GloVe; Discourse Processing: Anaphora and Coreference Resolution and Discourse Connectives. Machine Tr

Recurrent neural networks, LSTMs/GRUs, Neural Sequence Models, Contextualized Word Embeddings: TagLM, ELMO, ULMFIT, etc., Attention Mechanism (Code de

Transformers, Self-attention Mechanism, Sub-word tokenization, Positional encoding, Pre-trained Language Models (PLMs): BERT, GPT, etc. Fine-tuning an

Large Language Models (LLMs) Parameter Efficient Fine Tuning: Prefix-coding, LORA, QLORA, etc. Emergent Behavior: In-context learning, Instruction Fin

Natural Language Generation, Decoding schemes: greedy, Random sampling, Top-k, Top-p, Speculative sampling, etc.

NLP applications: QnA, Summarization, NLI, Fact-checking, etc. Retrieval Augmented generation (RAG). (Code demo: Summarisation)

Model Explainability: Attention maps, Attention-flow/rollout, Integrated gradients, etc. (Code demo: Attension visualization and Integrated gradients

Ethical and cultural considerations and biases.

Video Archive

Intelligence Hub