Understand and implement Huggingface-Models, LLMs, Vector Databases, RAG, Prompt Engineering, and more
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
Description
Join my comprehensive course on Natural Language Processing (NLP). The course is designed for both beginners and seasoned professionals. This course is your gateway to unlocking the immense potential of NLP in solving real-world challenges. It covers a wide range of different topics and brings you up to speed on implementing NLP solutions.
Get Instant Notification of New Courses on our Telegram channel.
Noteβ Make sure your ππππ¦π² cart has only this course you're going to enroll it now, Remove all other courses from the ππππ¦π² cart before Enrolling!
Course Highlights:
- NLP-Introduction
- Gain a solid understanding of the fundamental principles that govern Natural Language Processing and its applications.
- Basics of NLP
- Word Embeddings
- Transformers
- Apply Huggingface for Pre-Trained Networks
- Learn about Huggingface models and how to apply them to your needs
- Model Fine-Tuning
- Sometimes pre-trained networks are not sufficient, so you need to fine-tune an existing model on your specific task and / or dataset. In this section you will learn how.
- Vector Databases
- Vector Databases make it simple to query information from texts. You will learn how they work and how to implement vector databases.
- Tokenization
- Implement Vector DB with ChromaDB
- Multimodal Vector DB
- OpenAIΒ API
- OpenAI with ChatGPT provides a very powerful tool for NLP. You will learn how to make use of it via Python and integrating it in your workflow.
- Prompt Engineering
- Learn strategies to create efficient prompts
- Retrieval-Augmented Generation
- RAG Theory
- Implement RAG
- Capstone Project “Chatbot”
- create a chatbot to “chat” with a PDF document
- create a web application for the chatbot
- OpenΒ Source LLMs
- learn how to use OpenSource LLMs
- Data Augmentation
- Theory and Approaches of NLP Data Augmentation
- Implementation of Data Augmentation
English
language
Content
Course-Introduction
Course Scope (101)
Who am I?
How to work with The course (101)
How to get the material? (Coding)
How to get the material? (Alternate)
System Setup (101)
System Setup (Coding)
NLP-Introduction
Section Overview
NLP (101)
Word Embeddings (101)
Sentiment OHE Coding Intro
Sentiment OHE (Coding)
Word Embeddings with NN (101)
GloVe: Get Word Embedding (Coding)
GloVe: Find closest words (Coding)
GloVe: Word Analogy (Coding)
GloVe: Word Cluster (101)
GloVe Word (Coding)
Sentiment with Embedding (101)
Sentiment with Embedding (Coding)
Transformers (101)
Apply Huggingface for Pre-Trained Models
Section Overview
Huggingface (101)
Pipelines: General Use (101)
Text Classification (101)
Pipelines: General Use (Coding)
Named Entity Recognition (101)
Named Entity Recognition (Coding)
Question Answering (101)
Question Answering (Coding)
Text Summarization (101)
Text Summarization (Coding)
Translation (101)
Translation (Coding)
Fill-Mask (101)
Fill-Mask (Coding)
Zero-Shot Text Classification (101)
Zero-Shot Text Classification (Coding)
Model Finetuning
Section Overview
Simple Model (101)
Exploratory Data Analysis (Coding)
Simple Model (Coding)
Finetuning Model (101)
Huggingface Trainer (101)
Finetuning Model (Coding)
Saving Model to huggingface / Loading Model (Coding)
Vector Databases
Vector Databases (101)
Tokenization (101)
Tokenization (Practical)
Tokenization (Coding)
Bible Vector DB – The Full Picture
Bible Vector DB – Data Prep (Coding)
Bible Vector DB – Database Handling (Coding)
Exercise: Movies Vector DB
Solution: Movies Vector DB – Data Prep (Coding)
Solution: Movies Vector DB – DB-Setup (Coding)
Solution: Movies Vector DB – Query Function (Coding)
Multimodal Vector DB (101)
Multimodal Vector DB: Setup (Coding)
Multimodal Vector DB: Query (Coding)
OpenAI API
Section Overview
ChatGPT (101)
OpenAI API (101)
Get your API Key (Coding)
Python Package (101)
Python Package (Coding)
Rest APIs (101)
OpenAI WebUI (Coding)
Cost (101)
Prompt Engineering
Prompt Engineering (101)
Clear Instructions (Coding)
Personas (Coding)
Delimiters (Coding)
Divide into sub-tasks (Coding)
Provide Examples (Coding)
Control Output (Coding)
Retrieval-Augmented Generation (RAG)
RAG (101)
RAG Coding – The Final Result
RAG: Handling Vector DB (Coding)
RAG: Handling LLM (Coding)
RAG: Putting all together (Coding)
Capstone Project “Chatbot”
Webapp Climate Change Chatbot (101)
Webapp Climate Change Chatbot: Data Prep (Coding)
Webapp Climate Change Chatbot: Vector DB (Coding)
Webapp Climate Change Chatbot: RAG (Coding)
Webapp Climate Change Chatbot: Webapp (Coding)
Open Source LLMs
Open Source LLMs (101)
Open Source LLMs (Coding)
Data Augmentation
Data Augmentation (101)
Data Augmentation: Back-Translation (Coding)
Data Augmentation: Replacement with Synonyms (Coding)
Data Augmentation: Random Cropping (Coding)
Data Augmentation: Contextual Augmentation (Coding)
Data Augmentation: Word Embeddings (Coding)
Data Augmentation: Fill-Mask (Coding)
Final Section
Congratulations and Thank You!
Closing Remarks
Bonus Lecture