Yiga AI

Course 8 of 16

Natural Language Processing

Tokenisation, TF-IDF, Sentiment, and more.

Yiga AI

Intermediate en

Intermediate · Course · 4 weeks at 5 hrs/week

Duration
4 weeks
Effort
5 hrs/week
Lessons
20
Hands-on exercises
20
Projects
1
Language
en

What you’ll learn

Skills you can put to work.

Python for Data Science Refresher

Statistical Thinking

Supervised Learning with scikit-learn

Unsupervised Learning

Syllabus

Every module, step by step.

4 modules · 25 lessons · 17 hours · 1 graded project

1000 minutes of guided lesson time.

Sign in and enrol to unlock every lesson. The first lesson is open as a preview.

Module 1 · 6 lessons · 3.7 hoursTokenisationModule 1 of 4 in Natural Language Processing.

  1. Overview and set-up

    Overview and set-up — Tokenisation in Natural Language Processing.

    Lesson

    40 min

  2. Core concepts

    Core concepts — Tokenisation in Natural Language Processing.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Tokenisation in Natural Language Processing.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Tokenisation in Natural Language Processing.

    Lesson · Locked

    40 min

  5. Common pitfalls

    Common pitfalls — Tokenisation in Natural Language Processing.

    Lesson · Locked

    40 min

  6. Module quiz: Tokenisation

    Check what you learned in Tokenisation before moving on.

    Quiz · Locked

    20 min
Module 2 · 6 lessons · 3.7 hours LockedTF-IDFModule 2 of 4 in Natural Language Processing.

  1. Overview and set-up

    Overview and set-up — TF-IDF in Natural Language Processing.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — TF-IDF in Natural Language Processing.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — TF-IDF in Natural Language Processing.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — TF-IDF in Natural Language Processing.

    Lesson · Locked

    40 min

  5. Common pitfalls

    Common pitfalls — TF-IDF in Natural Language Processing.

    Lesson · Locked

    40 min

  6. Module quiz: TF-IDF

    Check what you learned in TF-IDF before moving on.

    Quiz · Locked

    20 min
Module 3 · 6 lessons · 3.7 hours LockedSentimentModule 3 of 4 in Natural Language Processing.

  1. Overview and set-up

    Overview and set-up — Sentiment in Natural Language Processing.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — Sentiment in Natural Language Processing.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Sentiment in Natural Language Processing.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Sentiment in Natural Language Processing.

    Lesson · Locked

    40 min

  5. Common pitfalls

    Common pitfalls — Sentiment in Natural Language Processing.

    Lesson · Locked

    40 min

  6. Module quiz: Sentiment

    Check what you learned in Sentiment before moving on.

    Quiz · Locked

    20 min
Module 4 · 7 lessons · 5.7 hours LockedTransformers introModule 4 of 4 in Natural Language Processing.

  1. Overview and set-up

    Overview and set-up — Transformers intro in Natural Language Processing.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — Transformers intro in Natural Language Processing.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Transformers intro in Natural Language Processing.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Transformers intro in Natural Language Processing.

    Lesson · Locked

    40 min

  5. Common pitfalls

    Common pitfalls — Transformers intro in Natural Language Processing.

    Lesson · Locked

    40 min

  6. Module quiz: Transformers intro

    Check what you learned in Transformers intro before moving on.

    Quiz · Locked

    20 min

  7. Graded project: Natural Language Processing

    Apply everything from Natural Language Processing in one graded, portfolio-ready build.

    Graded project · Locked

    120 min

Applied learning project

Make the learning visible.

Use Graded project: Natural Language Processing to apply this course to a practical challenge. You’ll finish with a clear solution and the reasoning behind your choices.

Apply the course’s core skillMake and explain practical decisionsCreate evidence you can share
Joel N., Career readiness

Your instructor

Joel N.

Career readiness

Connects technical learning to portfolios, interviews and confident career moves.

Amina Hassan, Yiga learner

“The track removed the uncertainty. I knew what to learn next and what to show employers.”

Amina Hassan

Career switcher, Dar es Salaam

Continue with

Your next best step.

Following the order of Data Scientist with Python.

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Overview and set-up

Natural Language Processing

40 min · Resume

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Course 10 in the track

Tree-Based Models & Gradient Boosting

Decision trees, Random forest, XGBoost, and more.

Intermediate · Course · 3 weeks at 5 hrs/week

Course 11 in the track

Experiment Design & A/B Testing

Design, Power, Analysis, and more.

Intermediate · Course · 3 weeks at 5 hrs/week

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