Learn how to apply BERT and GPT models for real-world NLP tasks with modern transformer workflows.
What You'll Learn
- Understand transformer architecture and attention mechanisms
- Use BERT for embeddings, classification, and text similarity
- Apply GPT-style models for generation and summarization
- Fine-tune transformers for downstream tasks
- Build pipelines using HuggingFace Transformers
Requirements
- Python
- PyTorch or TensorFlow
- NLP basics
Earn Your Certificate
Showcase your skills with a certificate! Complete the course, submit your project, and earn your certificate. Here's a sample of what you'll receive to show off your accomplishments.
Course Curriculum
4 Chapters
20 Lectures
41h 23m 10s
1. Transformer Foundationss
5 Lectures|1 Assignment|1 Quiz|4h 41m
1. What Are Transformers? Architecture Overview
2. Self-Attention Explained Clearly
3. Encoder vs Decoder vs Encoder–Decoder Models
4. Tokenization & Input Representation for Transformers
5. HuggingFace Model Zoo Walkthrough
6. Lesson 1
7. Analyze Transformer Inputs
8. Transformer Input Pipeline Guide
2. Applied BERT Workflows
5 Lectures|11h 27m
3. Applied GPT Workflows
5 Lectures|1 Assignment|1 Quiz|8h 15m
4. Real-World Transformer Projects
5 Lectures|17h 10s
Live Classes from the courses
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$129.00
Course Includes
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