Course Overview
- Course Title: LLMs Foundations: Tokenization and Word Embeddings Models
- Instructor: Nawas Naziru Adam (Robotics and AI Engineer)
- Target Audience:
- Aspiring developers in AI and NLP
- AI enthusiasts seeking foundational knowledge
- Professionals looking to deepen LLM and chatbot understanding
- Beginners with basic Python and neural network experience
- Prerequisites:
- Basic knowledge of Python programming
- Basic understanding of neural networks
Curriculum Highlights
- Key Topics Covered:
- Tokenization in LLMs and NLP
- Word embeddings models (theory and implementation)
- Mathematical foundations of LLMs (simplified)
- Building word embeddings with PyTorch
- Developing a "basic mini LLM" from scratch
- Real-world applications (e.g., question answering)
- Key Skills Learned:
- Implementing tokenization for text preprocessing
- Designing and training word embedding models
- Applying PyTorch for NLP tasks
- Understanding semantic meaning in vector spaces
- Prototyping a functional mini LLM
Course Format
- Duration: 6.5 hours of on-demand video
- Format: Self-paced online course (lifetime access)
- Resources:
- Mobile and TV access
- Closed captions
- Audio descriptions in existing audio
- Certificate of completion
Additional Information
- Course Language: English
- Student Enrollment: 3,790+ students
- Instructor Rating: 4.0/5 (based on 42 reviews)
- Focus Keywords: LLM training, NLP fundamentals, PyTorch tutorials, word embeddings, tokenization in AI, AI chatbot development, large language model course