skill~150 mindifficulty

Bag-of-Words & TF-IDF

Bag-of-words and TF-IDF are simple, fast baselines that still beat many neural models on small datasets and teach the vectorization mindset.

1

Learn it

Bag-of-words counts token occurrences into a sparse vector; TF-IDF rescales those counts so rare, informative terms weigh more than ubiquitous ones. You will use scikit-learn's CountVectorizer and TfidfVectorizer, tune n-gram ranges and min/max document frequency, and inspect the feature space.

By the end you’ll be able to

  • Represent documents as sparse count and TF-IDF vectors
  • Understand why IDF down-weights common terms; use n-grams

Free resources

2

Practice

Vectorize a corpus with TfidfVectorizer, then print the 15 highest-weighted terms for two contrasting documents.

3

Build it — your output

A notebook that converts a text corpus into a TF-IDF matrix and reports top terms per class.

4

Prove it — check yourself

You’re ready to move on when you can honestly say yes to each:

  • Explain the TF and IDF components in plain language
  • Predict how min_df/max_df changes vocabulary size
  • Contrast when raw counts beat TF-IDF

Do it with AI

Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).

The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.

  • Understand itClaude / ChatGPT / Gemini

    I'm a beginner learning "Bag-of-Words & TF-IDF". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.

  • PracticeClaude / ChatGPT / Gemini

    Give me 3 short practice problems on "Bag-of-Words & TF-IDF" (my goal: represent documents as sparse count and tf-idf vectors), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.

  • Get unstuckClaude / ChatGPT / Gemini

    I'm stuck on this exercise about "Bag-of-Words & TF-IDF": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.

  • Build & reviewCursor / Copilot

    Here is my work for a "Bag-of-Words & TF-IDF" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.

  • Prove itClaude / ChatGPT / Gemini

    Quiz me on "Bag-of-Words & TF-IDF". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.

My notes