Applied Generative AI and Natural Language Processing
Understand Generative AI, Prompt Engineering, Huggingface-Models, LLMs, Vector Databases, RAG, OpenAI, Claude, Llama2
4.51 (370 reviews)
15 011
students
10 hours
content
Sep 2024
last update
$79.99
regular price
What you will learn
Introduction to Natural Language Processing (NLP)
model implementation based on huggingface-models
working with OpenAI
Vector Databases
Multimodal Vector Databases
Retrieval-Augmented-Generation (RAG)
Real-World Applications and Case Studies
implement Zero-Shot Classification, Text Classification, Text Generation
fine-tune models
data augmentation
Prompt Engineering
Zero-Shot Promping
Few-Shot Prompting
Chain-of-Thought (Few-Shot CoT, Zero-Shot CoT)
Self-Consistency Chain-of-Thought
Prompt Chaining
Tree-of-Thought
Self-Feedback
Self-Critique
Claude 3
Open Source Models, e.g. LLama 2, Mistral
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Our Verdict
Applied Generative AI and Natural Language Processing is a thorough course that delves into the essentials of NLP while also exploring advanced topics like RAG and Prompt Engineering. With ample hands-on exercises, it offers a valuable learning experience; however, occasional shortcomings in explanation depth, coding session structure, and deployment guidance detract from its overall impact.
What We Liked
- Broad coverage of Generative AI and Natural Language Processing, including cutting-edge topics like Retrieval-Augmented Generation (RAG) and advanced Prompt Engineering techniques
- Hands-on approach with numerous practical exercises, enabling students to immediately apply their newly acquired knowledge
- Comprehensive introduction to Huggingface models and Vector Databases, empowering learners to implement NLP solutions effectively
- In-depth exploration of OpenAI API and integration with Python, unlocking the full potential of ChatGPT for NLP tasks
Potential Drawbacks
- Lacks detailed explanation of some concepts, such as the internal workings of Word Embeddings and syntactic/semantic relationships
- Coding sessions could benefit from a more step-by-step breakdown, making them more accessible to beginners
- Occasional issues with environment setup hinder the learning experience for some students
- Insufficient guidance on deploying NLP models in web applications, leaving learners searching for additional resources
5762060
udemy ID
13/01/2024
course created date
02/03/2024
course indexed date
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