Module 8 · 100 Days of NLP

Module 8: Duplicate Question Detection (Quora Case Study)

Solve intent matching: construct feature vectors for question duplicate pairs. Compute Cosine, Jaccard, and Fuzzy similarity metrics modeled using XGBoost.

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

Day 81

Similarity Problem Framing

Why this matters

Similarity Problem Framing: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Similarity Problem Framing 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Quora Dataset EDA

Day 82

Quora Dataset EDA

Why this matters

Quora Dataset EDA: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Quora Dataset EDA 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Cosine Similarity

Day 83

Cosine Similarity

Why this matters

Cosine Similarity: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Cosine similarity

$$\cos(\mathbf{a}, \mathbf{b}) = \frac{\mathbf{a} \cdot \mathbf{b}}{\|\mathbf{a}\| \|\mathbf{b}\|}$$

Key takeaways

Define Cosine Similarity 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Jaccard Similarity

Day 84

Jaccard Similarity

Why this matters

Jaccard Similarity: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Jaccard Similarity 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Edit Distance

Day 85

Edit Distance

Why this matters

Edit Distance: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Edit Distance 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Fuzzy Matching

Day 86

Fuzzy Matching

Why this matters

Fuzzy Matching: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Fuzzy Matching 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Feature Engineering

Day 87

Feature Engineering

Why this matters

Feature Engineering: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Feature Engineering 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: TF-IDF Features

Day 88

TF-IDF Features

Why this matters

TF-IDF Features: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define TF-IDF Features 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Word2Vec Features

Day 89

Word2Vec Features

Why this matters

Word2Vec Features: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

Word2Vec Features is a core topic in the 100 Days of NLP curriculum. This lesson connects theory to practical pipelines you will build in projects.

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Word2Vec Features 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

Q: Explain word2vec features in one minute. A: State definition, when to use it, and one failure mode.
Q: How does word2vec features fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

Basic: Define Word2Vec Features and give one real product example.
Intermediate: Implement or sketch a minimal example for Word2Vec Features.
Advanced: Compare Word2Vec Features to the previous topic on the same dataset.

Recap

You can explain word2vec features clearly.
You know one common mistake and how to avoid it.
You see how this connects to the next topic.

Next: XGBoost Classifier

Day 90

XGBoost Classifier

Why this matters

XGBoost Classifier: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

import xgboost as xgb
from sklearn.model_selection import train_test_split

# X: TF-IDF or embedding features, y: duplicate label 0/1
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
model = xgb.XGBClassifier(n_estimators=200, max_depth=6)
model.fit(X_train, y_train)
print(model.score(X_val, y_val))

Key takeaways

Define XGBoost Classifier 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Model Evaluation

Day 91

Model Evaluation

Why this matters

Model Evaluation: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Model Evaluation 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Error Analysis

Day 92

Error Analysis

Why this matters

Error Analysis: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Error 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Threshold Tuning

Day 93

Threshold Tuning

Why this matters

Threshold Tuning: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Threshold Tuning 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Ensemble Approach

Day 94

Ensemble Approach

Why this matters

Ensemble Approach: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Ensemble Approach 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: BERT Embeddings Intro

Day 95

BERT Embeddings Intro

Why this matters

BERT Embeddings Intro: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

BERT-style bi-encoders or cross-encoders improve semantic matching but add latency — trade off against TF-IDF + XGBoost baselines.

Key takeaways

Define BERT Embeddings 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Sentence Transformers

Day 96

Sentence Transformers

Why this matters

Sentence Transformers: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Sentence Transformers 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Deployment API

Day 97

Deployment API

Why this matters

Deployment API: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Deployment API 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Scalability Considerations

Day 98

Scalability Considerations

Why this matters

Scalability Considerations: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Scalability Considerations 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Capstone Project

Day 99

Capstone Project

Why this matters

Capstone Project: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Key takeaways

Define Capstone 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Final Review 🎓

Day 100

Final Review 🎓

Why this matters

Final Review 🎓: Duplicate question detection combines similarity metrics, features, and deployment — a full capstone.

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

Quora duplicate question pairs

Given two questions, predict if they are semantically duplicate. Combine similarity features (cosine TF-IDF, Jaccard tokens, edit distance) with classifiers like XGBoost or neural encoders.

Congratulations! You have covered foundations → pipeline → vectors → classification → sequence models → duplicate detection capstone. Revisit weak days and ship one end-to-end notebook.

Key takeaways

Define Final Review 🎓 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

Tuning threshold on training set only.
Leakage from duplicate pairs in both train and test splits.
Using only one similarity metric without error analysis.

Interview checkpoints

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

Practice

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

Recap

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

Next: Next module

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