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Module 3 · 100 Days of NLP

Module 3: Text Preprocessing Techniques

Master text normalization: tokenization, lowercasing, stopword stripping, and Porter stemming vs. morphological lemmatization.

⏱ 15 Min Read • Author: GenAIWallah Team • Updated: May 2026
Day 26

Tokenization Basics

Why this matters

Tokenization Basics: This NLP concept connects theory to the models and APIs you will use in projects.

Tokenization Basics is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Tokenization Basics clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain tokenization basics in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does tokenization basics fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Tokenization Basics and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Tokenization Basics.
  3. Advanced: Compare Tokenization Basics to the previous topic on the same dataset.

Recap

  • You can explain tokenization basics clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Word Tokenization

Day 27

Word Tokenization

Why this matters

Word Tokenization: This NLP concept connects theory to the models and APIs you will use in projects.

Word Tokenization is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Word Tokenization clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain word tokenization in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does word tokenization fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Word Tokenization and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Word Tokenization.
  3. Advanced: Compare Word Tokenization to the previous topic on the same dataset.

Recap

  • You can explain word tokenization clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Subword Tokenization

Day 28

Subword Tokenization

Why this matters

Subword Tokenization: This NLP concept connects theory to the models and APIs you will use in projects.

Subword Tokenization is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Subword Tokenization clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain subword tokenization in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does subword tokenization fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Subword Tokenization and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Subword Tokenization.
  3. Advanced: Compare Subword Tokenization to the previous topic on the same dataset.

Recap

  • You can explain subword tokenization clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Stopword Removal

Day 29

Stopword Removal

Why this matters

Stopword Removal: This NLP concept connects theory to the models and APIs you will use in projects.

Stopword Removal is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Stopword Removal clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain stopword removal in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does stopword removal fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Stopword Removal and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Stopword Removal.
  3. Advanced: Compare Stopword Removal to the previous topic on the same dataset.

Recap

  • You can explain stopword removal clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Stemming (Porter)

Day 30

Stemming (Porter)

Why this matters

Stemming (Porter): This NLP concept connects theory to the models and APIs you will use in projects.

Stemming (Porter) is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Stem vs lemma

"running" → stem run (Porter) vs lemma run (WordNet-aware). Lemmatization needs POS; stemming is faster but cruder.

Key takeaways

  • Define Stemming (Porter) clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain stemming (porter) in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does stemming (porter) fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Stemming (Porter) and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Stemming (Porter).
  3. Advanced: Compare Stemming (Porter) to the previous topic on the same dataset.

Recap

  • You can explain stemming (porter) clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Lemmatization

Day 31

Lemmatization

Why this matters

Lemmatization: This NLP concept connects theory to the models and APIs you will use in projects.

Lemmatization is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Stem vs lemma

"running" → stem run (Porter) vs lemma run (WordNet-aware). Lemmatization needs POS; stemming is faster but cruder.

Key takeaways

  • Define Lemmatization clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain lemmatization in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does lemmatization fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Lemmatization and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Lemmatization.
  3. Advanced: Compare Lemmatization to the previous topic on the same dataset.

Recap

  • You can explain lemmatization clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Regex Cleaning

Day 32

Regex Cleaning

Why this matters

Regex Cleaning: Production NLP is a pipeline — bad cleaning or leakage upstream ruins the best model.

Regex Cleaning is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Regex Cleaning clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Fitting vectorizers on the full dataset including test data.
  • Different preprocessing at training vs inference.
  • No versioning of tokenizer/vocabulary artifacts.

Interview checkpoints

  • Q: Explain regex cleaning in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does regex cleaning fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Regex Cleaning and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Regex Cleaning.
  3. Advanced: Compare Regex Cleaning to the previous topic on the same dataset.

Recap

  • You can explain regex cleaning clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Sentence Splitting

Day 33

Sentence Splitting

Why this matters

Sentence Splitting: This NLP concept connects theory to the models and APIs you will use in projects.

Sentence Splitting is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Sentence Splitting clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain sentence splitting in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does sentence splitting fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Sentence Splitting and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Sentence Splitting.
  3. Advanced: Compare Sentence Splitting to the previous topic on the same dataset.

Recap

  • You can explain sentence splitting clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Custom Pipelines

Day 34

Custom Pipelines

Why this matters

Custom Pipelines: Production NLP is a pipeline — bad cleaning or leakage upstream ruins the best model.

Custom Pipelines is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Custom Pipelines clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Fitting vectorizers on the full dataset including test data.
  • Different preprocessing at training vs inference.
  • No versioning of tokenizer/vocabulary artifacts.

Interview checkpoints

  • Q: Explain custom pipelines in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does custom pipelines fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Custom Pipelines and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Custom Pipelines.
  3. Advanced: Compare Custom Pipelines to the previous topic on the same dataset.

Recap

  • You can explain custom pipelines clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Preprocessing Project

Day 35

Preprocessing Project

Why this matters

Preprocessing Project: This NLP concept connects theory to the models and APIs you will use in projects.

Preprocessing Project is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Preprocessing intuition

Text is messy: unicode, HTML, emojis, typos. Preprocessing trades recall (keep signal) vs noise (remove junk). Always fit vocabulary/statistics on training data only.

Key takeaways

  • Define Preprocessing Project clearly and state when to use it.
  • Connect this topic to the previous and next day in the curriculum.
  • Validate with a small code experiment or worked numeric example.

Common mistakes

  • Skipping train/validation split discipline.
  • Ignoring inference latency and memory.
  • No error analysis on misclassified examples.

Interview checkpoints

  • Q: Explain preprocessing project in one minute. A: State definition, when to use it, and one failure mode.
  • Q: How does preprocessing project fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

  1. Basic: Define Preprocessing Project and give one real product example.
  2. Intermediate: Implement or sketch a minimal example for Preprocessing Project.
  3. Advanced: Compare Preprocessing Project to the previous topic on the same dataset.

Recap

  • You can explain preprocessing project clearly.
  • You know one common mistake and how to avoid it.
  • You see how this connects to the next topic.

Next: Next module

← Module 2: Pipeline Module 4: Text Vectorization →