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 and Generative AI in solving real-world challenges. It covers a wide range of different topics and brings you up to speed on implementing NLP solutions.
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
-
-
OpenAI 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
-
-
Advanced Prompt Engineering
-
Few-Shot Prompting
-
Chain-of-Thought
-
Self-Consistency Chain-of-Thought
-
Prompt Chaining
-
Reflection
-
Tree-of-Thought
-
Self-Feedback
-
Self-Critique
-
-
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
-
-
Open Source LLMs
-
learn how to use OpenSource LLMs
-
Meta Llama 2
-
Mistral Mixtral
-
-
Data Augmentation
-
Theory and Approaches of NLP Data Augmentation
-
Implementation of Data Augmentation
-
NLP-Introduction
Apply Huggingface for Pre-Trained Models
-
8Section Overview
-
9NLP (101)
-
10Word Embeddings (101)
-
11Sentiment OHE Coding Intro
-
12Sentiment OHE (Coding)
-
13Word Embeddings with NN (101)
-
14GloVe: Get Word Embedding (Coding)
-
15GloVe: Find closest words (Coding)
-
16GloVe: Word Analogy (Coding)
-
17GloVe: Word Cluster (101)
-
18GloVe Word (Coding)
-
19Sentiment with Embedding (101)
-
20Sentiment with Embedding (Coding)
-
21Transformers (101)
Model Finetuning
-
22Section Overview
-
23Huggingface (101)
-
24Pipelines: General Use (101)
-
25Text Classification (101)
-
26Pipelines: General Use (Coding)
-
27Named Entity Recognition (101)
-
28Named Entity Recognition (Coding)
-
29Question Answering (101)
-
30Question Answering (Coding)
-
31Text Summarization (101)
-
32Text Summarization (Coding)
-
33Translation (101)
-
34Translation (Coding)
-
35Fill-Mask (101)
-
36Fill-Mask (Coding)
-
37Zero-Shot Text Classification (101)
-
38Zero-Shot Text Classification (Coding)
Vector Databases
OpenAI API
-
47Vector Databases (101)
-
48Tokenization (101)
-
49Tokenization (Practical)
-
50Tokenization (Coding)
-
51Bible Vector DB - The Full Picture
-
52Bible Vector DB - Data Prep (Coding)
-
53Bible Vector DB - Database Handling (Coding)
-
54Exercise: Movies Vector DB
-
55Solution: Movies Vector DB - Data Prep (Coding)
-
56Solution: Movies Vector DB - DB-Setup (Coding)
-
57Solution: Movies Vector DB - Query Function (Coding)
-
58Multimodal Vector DB (101)
-
59Multimodal Vector DB: Setup (Coding)
-
60Multimodal Vector DB: Query (Coding)