Course 2 of 12

NLP with Transformers

Attention, BERT, Fine-tuning, 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.

Machine Learning Foundations

Deep Learning with PyTorch

Computer Vision

NLP with Transformers

Syllabus

Every module, step by step.

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

1000 minutes of guided lesson time.

Module 1 · 6 lessons · 3.7 hoursAttentionModule 1 of 4 in NLP with Transformers.

Overview and set-up
Overview and set-up — Attention in NLP with Transformers.
Lesson
40 min
Core concepts
Core concepts — Attention in NLP with Transformers.
Lesson · Locked
40 min
Worked example
Worked example — Attention in NLP with Transformers.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Attention in NLP with Transformers.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Attention in NLP with Transformers.
Lesson · Locked
40 min
Module quiz: Attention
Check what you learned in Attention before moving on.
Quiz · Locked
20 min

Module 2 · 6 lessons · 3.7 hours LockedBERTModule 2 of 4 in NLP with Transformers.

Overview and set-up
Overview and set-up — BERT in NLP with Transformers.
Lesson · Locked
40 min
Core concepts
Core concepts — BERT in NLP with Transformers.
Lesson · Locked
40 min
Worked example
Worked example — BERT in NLP with Transformers.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — BERT in NLP with Transformers.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — BERT in NLP with Transformers.
Lesson · Locked
40 min
Module quiz: BERT
Check what you learned in BERT before moving on.
Quiz · Locked
20 min

Module 3 · 6 lessons · 3.7 hours LockedFine-tuningModule 3 of 4 in NLP with Transformers.

Overview and set-up
Overview and set-up — Fine-tuning in NLP with Transformers.
Lesson · Locked
40 min
Core concepts
Core concepts — Fine-tuning in NLP with Transformers.
Lesson · Locked
40 min
Worked example
Worked example — Fine-tuning in NLP with Transformers.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Fine-tuning in NLP with Transformers.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Fine-tuning in NLP with Transformers.
Lesson · Locked
40 min
Module quiz: Fine-tuning
Check what you learned in Fine-tuning before moving on.
Quiz · Locked
20 min

Module 4 · 7 lessons · 5.7 hours LockedText classificationModule 4 of 4 in NLP with Transformers.

Overview and set-up
Overview and set-up — Text classification in NLP with Transformers.
Lesson · Locked
40 min
Core concepts
Core concepts — Text classification in NLP with Transformers.
Lesson · Locked
40 min
Worked example
Worked example — Text classification in NLP with Transformers.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Text classification in NLP with Transformers.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Text classification in NLP with Transformers.
Lesson · Locked
40 min
Module quiz: Text classification
Check what you learned in Text classification before moving on.
Quiz · Locked
20 min
Graded project: NLP with Transformers
Apply everything from NLP with Transformers in one graded, portfolio-ready build.
Graded project · Locked
120 min

Applied learning project

Make the learning visible.

Use Graded project: NLP with Transformers 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

Your instructor

Mariam K.

Software & cloud

Helps new developers build reliable products through clear explanations and guided practice.

“I use what I learned in my work now, not in some distant future project.”
Diana Nsiiza
Data analyst, Entebbe

Continue with

Your next best step.

Following the order of Machine Learning Engineer.

See the full chain

Next lesson

Overview and set-up

NLP with Transformers

40 min · Resume

Course 3 in the track

Project: Fraud Detection System End-to-End

Data, Model, Deployment, and more.

Advanced · Course · 4 weeks at 5 hrs/week

Course 4 in the track

Reinforcement Learning Intro

Agents & environments, Q-learning, Policy gradients, and more.

Advanced · Course · 3 weeks at 5 hrs/week

Course 5 in the track

Computer Vision

Image classification, Object detection, Transfer learning, and more.

Intermediate · Course · 4 weeks at 5 hrs/week

Learner reviews

What learners say.

Write a review

Loading reviews…

Previous courseMLOps Fundamentals Next courseProject: Fraud Detection System End-to-End

Keep going

Other courses in this track.

View full track

20 lessons · 16.7h · Intermediate

MLOps Fundamentals

Experiment tracking (MLflow), Pipelines, Versioning, and more.

24 lessons · 19.3h · Advanced

Project: Fraud Detection System End-to-End

Data, Model, Deployment, and more.

16 lessons · 14h · Advanced

Reinforcement Learning Intro

Agents & environments, Q-learning, Policy gradients, and more.

21 lessons · 16.8h · Intermediate

Computer Vision

Image classification, Object detection, Transfer learning, and more.