Module 6 · 100 Days of NLP

Module 6: Text Classification Models

Master text classification engines. Study Naive Bayes with Laplace corrections, Logistic Regression probability optimization, and Support Vector margins.

⏱ 35 Min Read • Author: GenAIWallah Team • Updated: May 2026

Day 54

Text Classification Intro

Why this matters

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

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

Text classification

Key takeaways

Define Text Classification Intro 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 text classification intro in one minute. A: State definition, when to use it, and one failure mode.
Q: How does text classification intro fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

Basic: Define Text Classification Intro and give one real product example.
Intermediate: Implement or sketch a minimal example for Text Classification Intro.
Advanced: Compare Text Classification Intro to the previous topic on the same dataset.

Recap

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

Next: Naive Bayes NLP

Day 55

Naive Bayes NLP

Why this matters

Naive Bayes NLP: Text classifiers power spam filters, sentiment, intent detection, and routing.

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

Text classification

Multinomial Naive Bayes

$$\hat{y} = \arg\max_y P(y) \prod_i P(x_i \mid y)$$

Independence assumption is wrong but often works well for high-dimensional sparse text.

Key takeaways

Define Naive Bayes NLP 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

Accuracy on imbalanced classes without F1 or PR-AUC.
Naive Bayes with correlated features without understanding independence assumption.
No baseline (majority class) before complex models.

Interview checkpoints

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

Practice

Basic: Define Naive Bayes NLP and give one real product example.
Intermediate: Implement or sketch a minimal example for Naive Bayes NLP.
Advanced: Compare Naive Bayes NLP to the previous topic on the same dataset.

Recap

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

Next: Laplace Smoothing

Day 56

Laplace Smoothing

Why this matters

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

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

Text classification

Key takeaways

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

Practice

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

Recap

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

Next: Logistic Regression Text

Day 57

Logistic Regression Text

Why this matters

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

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

Text classification

Key takeaways

Define Logistic Regression Text 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 logistic regression text in one minute. A: State definition, when to use it, and one failure mode.
Q: How does logistic regression text fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

Basic: Define Logistic Regression Text and give one real product example.
Intermediate: Implement or sketch a minimal example for Logistic Regression Text.
Advanced: Compare Logistic Regression Text to the previous topic on the same dataset.

Recap

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

Next: SVM for Text

Day 58

SVM for Text

Why this matters

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

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

Text classification

Key takeaways

Define SVM for Text 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 svm for text in one minute. A: State definition, when to use it, and one failure mode.
Q: How does svm for text fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

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

Recap

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

Next: Multiclass Classification

Day 59

Multiclass Classification

Why this matters

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

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

Text classification

Key takeaways

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

Practice

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

Recap

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

Next: Confusion Matrix NLP

Day 60

Confusion Matrix NLP

Why this matters

Confusion Matrix NLP: NLP foundations explain why language is ambiguous and which tasks exist before you touch models.

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

Text classification

Key takeaways

Define Confusion Matrix NLP 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

Treating NLP as only chatbots (ignoring search, extraction, classification).
Skipping linguistic levels (lexical vs syntactic vs semantic).
Assuming English-only tokenization rules apply everywhere.

Interview checkpoints

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

Practice

Basic: Define Confusion Matrix NLP and give one real product example.
Intermediate: Implement or sketch a minimal example for Confusion Matrix NLP.
Advanced: Compare Confusion Matrix NLP to the previous topic on the same dataset.

Recap

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

Next: Precision & Recall

Day 61

Precision & Recall

Why this matters

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

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

Text classification

Key takeaways

Define Precision & Recall 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 precision & recall in one minute. A: State definition, when to use it, and one failure mode.
Q: How does precision & recall fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

Basic: Define Precision & Recall and give one real product example.
Intermediate: Implement or sketch a minimal example for Precision & Recall.
Advanced: Compare Precision & Recall to the previous topic on the same dataset.

Recap

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

Next: Sentiment Analysis

Day 62

Sentiment Analysis

Why this matters

Sentiment Analysis: Text classifiers power spam filters, sentiment, intent detection, and routing.

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

Text classification

Sentiment: binary or fine-grained stars; watch sarcasm and domain shift (product vs movie reviews).

Key takeaways

Define Sentiment Analysis 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

Accuracy on imbalanced classes without F1 or PR-AUC.
Naive Bayes with correlated features without understanding independence assumption.
No baseline (majority class) before complex models.

Interview checkpoints

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

Practice

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

Recap

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

Next: Spam Detection

Day 63

Spam Detection

Why this matters

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

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

Text classification

Key takeaways

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

Practice

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

Recap

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

Next: Topic Classification

Day 64

Topic Classification

Why this matters

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

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

Text classification

Key takeaways

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

Practice

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

Recap

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

Next: Classification Project

Day 65

Classification Project

Why this matters

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

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

Text classification

Key takeaways

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

Practice

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

Recap

You can explain classification 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 5: Word2Vec Module 7: POS Tagging →