1. Python Fundamentals
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Data types: int, float, string, list, tuple, dictionary
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Control structures: if statements, loops
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Functions and modules
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Basic error handling
2. Intermediate Python Programming
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Object-oriented programming (classes and objects)
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List comprehensions
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Lambda functions
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File I/O operations
Prompting Engineering
3. Introduction
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LLM Settings
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Basics of Prompting
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Prompt Elements
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General Tips for Designing Prompts
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Examples of Prompts
NLP COURSE
4. Setup
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Introduction
5. Transformer Models
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Introduction
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Natural Language Processing
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Transformers, what can they do?
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How do Transformers work?
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Encoder Models
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Decoder Models
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Sequence-to-Sequence Models
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Bias and Limitations
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Summary
? End-of-chapter quiz
6. Using ???? Transformers
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Introduction
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Behind the pipeline
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Models
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Tokenizers
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Handling multiple sequences
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Putting it all together
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Basic usage completed!
? End-of-chapter quiz
7. Main NLP Tasks
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Introduction
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Translation
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Summarization
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Question answering
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Mastering NLP
? End-of-chapter quiz
AUDIO COURSE
8. Working with Audio Data
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Introduction to audio data
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Load and explore an audio dataset
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Preprocessing an audio dataset
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Streaming audio data
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Supplemental Reading and Resources
? End-of-chapter quiz
9. A gentle introduction to audio applications
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Audio classification with a pipeline
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Automatic speech recognition with a pipeline
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Audio generation with a pipeline
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Hands-on exercise
10. Putting it all together
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Speech-to-speech translation
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Creating a voice assistant
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Supplemental Reading and Resources
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Hands-on exercise
Computer Vision
11. Fundamentals
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Vision
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Image
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Imaging
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Imaging in Real-life
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What Is Computer Vision
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Applications of Computer Vision
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Pre-processing for Computer Vision Tasks
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Feature Description
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Feature Matching
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Real-world Applications of Feature Extraction in Computer Vision
12. Basic CV Tasks
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Introduction
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Introduction
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Image Segmentation
13. Video and Video Processing
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Introduction
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Video Processing Basics
14. Ethics and Biases
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Exploring Ethical Foundations in CV Models
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Introduction
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Ethics and Bias in AI
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Hugging Face’s efforts: Ethics and Society ????
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Supplementary reading and resources ????
15. AI Cookbook
The AI Cookbook is a collection of notebooks illustrating practical aspects of building AI applications and solving various machine learning tasks using opensource tools and models.