Module 4 · 100 Days of NLP

Module 4: Text Vectorization & TF-IDF

Examine traditional text representation structures: One-Hot vectors, Bag-of-Words, N-grams, and term weighting via TF-IDF scaling algorithms.

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

Day 36

One-Hot Encoding

Why this matters

One-Hot Encoding: This NLP concept connects theory to the models and APIs you will use in projects.

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define One-Hot Encoding 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 one-hot encoding in one minute. A: State definition, when to use it, and one failure mode.
Q: How does one-hot encoding fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

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

Recap

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

Next: Bag of Words

Day 37

Bag of Words

Why this matters

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

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

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

Practice

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

Recap

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

Next: N-grams

Day 38

N-grams

Why this matters

N-grams: How you represent text (BoW, TF-IDF, embeddings) dominates classical NLP baselines.

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define N-grams 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

Using raw counts when IDF would down-weight common terms.
Huge vocabularies without min_df/max_features.
Comparing cosine similarity on unnormalized vectors.

Interview checkpoints

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

Practice

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

Recap

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

Next: TF-IDF Theory

Day 39

TF-IDF Theory

Why this matters

TF-IDF Theory: How you represent text (BoW, TF-IDF, embeddings) dominates classical NLP baselines.

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

Vectorization

TF-IDF

$$\mathrm{TFIDF}(t,d,D) = \mathrm{TF}(t,d) \times \mathrm{IDF}(t,D), \quad \mathrm{IDF}(t,D) = \log \frac{|D|}{|\{d \in D : t \in d\}|}$$

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define TF-IDF Theory 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

Using raw counts when IDF would down-weight common terms.
Huge vocabularies without min_df/max_features.
Comparing cosine similarity on unnormalized vectors.

Interview checkpoints

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

Practice

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

Recap

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

Next: TF-IDF with Sklearn

Day 40

TF-IDF with Sklearn

Why this matters

TF-IDF with Sklearn: How you represent text (BoW, TF-IDF, embeddings) dominates classical NLP baselines.

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

Vectorization

from sklearn.feature_extraction.text import TfidfVectorizer

docs = [
    "the cat sat on the mat",
    "the dog sat on the log",
]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)
print(vectorizer.get_feature_names_out())
print(X.toarray())

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define TF-IDF with Sklearn 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

Using raw counts when IDF would down-weight common terms.
Huge vocabularies without min_df/max_features.
Comparing cosine similarity on unnormalized vectors.

Interview checkpoints

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

Practice

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

Recap

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

Next: Co-occurrence Matrix

Day 41

Co-occurrence Matrix

Why this matters

Co-occurrence Matrix: This NLP concept connects theory to the models and APIs you will use in projects.

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define Co-occurrence Matrix 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 co-occurrence matrix in one minute. A: State definition, when to use it, and one failure mode.
Q: How does co-occurrence matrix fit in an NLP pipeline? A: Name inputs, outputs, and what breaks if this step is wrong.

Practice

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

Recap

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

Next: Sparse Representations

Day 42

Sparse Representations

Why this matters

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

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

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

Practice

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

Recap

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

Next: CountVectorizer

Day 43

CountVectorizer

Why this matters

CountVectorizer: How you represent text (BoW, TF-IDF, embeddings) dominates classical NLP baselines.

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define CountVectorizer 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

Using raw counts when IDF would down-weight common terms.
Huge vocabularies without min_df/max_features.
Comparing cosine similarity on unnormalized vectors.

Interview checkpoints

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

Practice

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

Recap

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

Next: TF-IDF Search Engine

Day 44

TF-IDF Search Engine

Why this matters

TF-IDF Search Engine: How you represent text (BoW, TF-IDF, embeddings) dominates classical NLP baselines.

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define TF-IDF Search Engine 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

Using raw counts when IDF would down-weight common terms.
Huge vocabularies without min_df/max_features.
Comparing cosine similarity on unnormalized vectors.

Interview checkpoints

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

Practice

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

Recap

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

Next: Vectorization Project

Day 45

Vectorization Project

Why this matters

Vectorization Project: How you represent text (BoW, TF-IDF, embeddings) dominates classical NLP baselines.

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

Vectorization

Sparse bag-of-words and TF-IDF remain strong baselines for search and classification before neural embeddings.

Key takeaways

Define Vectorization 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

Using raw counts when IDF would down-weight common terms.
Huge vocabularies without min_df/max_features.
Comparing cosine similarity on unnormalized vectors.

Interview checkpoints

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

Practice

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

Recap

You can explain vectorization 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 3: Preprocessing Module 5: Word Embeddings →