Applied Natural Language Processing With Python

Understand and implement Huggingface-Models, LLMs, Vector Databases, RAG, Prompt Engineering, and more

What you'll 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

Requirements

Python Basic knowledge

Basic knowledge on How Deeplearning works

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.Course Highlights:NLP-IntroductionGain a solid understanding of the fundamental principles that govern Natural Language Processing and its applications.Basics of NLPWord EmbeddingsTransformersApply Huggingface for Pre-Trained NetworksLearn about Huggingface models and how to apply them to your needsModel Fine-TuningSometimes 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 DatabasesVector Databases make it simple to query information from texts. You will learn how they work and how to implement vector databases.TokenizationImplement Vector DB with ChromaDBMultimodal Vector DBOpenAI APIOpenAI 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 EngineeringLearn strategies to create efficient promptsRetrieval-Augmented GenerationRAG TheoryImplement RAGCapstone Project "Chatbot"create a chatbot to "chat" with a PDF documentcreate a web application for the chatbotOpen Source LLMslearn how to use OpenSource LLMsData AugmentationTheory and Approaches of NLP Data AugmentationImplementation of Data Augmentation

Overview

Section 1: Course-Introduction

Lecture 1 Who am I?

Lecture 2 Course Scope (101)

Lecture 3 How to work with The course (101)

Lecture 4 How to get the material? (Coding)

Lecture 5 How to get the material? (Alternate)

Lecture 6 System Setup (101)

Lecture 7 System Setup (Coding)

Section 2: NLP-Introduction

Lecture 8 Section Overview

Lecture 9 NLP (101)

Lecture 10 Word Embeddings (101)

Lecture 11 Sentiment OHE Coding Intro

Lecture 12 Sentiment OHE (Coding)

Lecture 13 Word Embeddings with NN (101)

Lecture 14 GloVe: Get Word Embedding (Coding)

Lecture 15 GloVe: Find closest words (Coding)

Lecture 16 GloVe: Word Analogy (Coding)

Lecture 17 GloVe: Word Cluster (101)

Lecture 18 GloVe Word (Coding)

Lecture 19 Sentiment with Embedding (101)

Lecture 20 Sentiment with Embedding (Coding)

Lecture 21 Transformers (101)

Section 3: Apply Huggingface for Pre-Trained Models

Lecture 22 Section Overview

Lecture 23 Huggingface (101)

Lecture 24 Pipelines: General Use (101)

Lecture 25 Text Classification (101)

Lecture 26 Pipelines: General Use (Coding)

Lecture 27 Named Entity Recognition (101)

Lecture 28 Named Entity Recognition (Coding)

Lecture 29 Question Answering (101)

Lecture 30 Question Answering (Coding)

Lecture 31 Text Summarization (101)

Lecture 32 Text Summarization (Coding)

Lecture 33 Translation (101)

Lecture 34 Translation (Coding)

Lecture 35 Fill-Mask (101)

Lecture 36 Fill-Mask (Coding)

Lecture 37 Zero-Shot Text Classification (101)

Lecture 38 Zero-Shot Text Classification (Coding)

Section 4: Model Finetuning

Lecture 39 Section Overview

Lecture 40 Simple Model (101)

Lecture 41 Exploratory Data Analysis (Coding)

Lecture 42 Simple Model (Coding)

Lecture 43 Finetuning Model (101)

Lecture 44 Huggingface Trainer (101)

Lecture 45 Finetuning Model (Coding)

Lecture 46 Saving Model to huggingface / Loading Model (Coding)

Section 5: Vector Databases

Lecture 47 Vector Databases (101)

Lecture 48 Tokenization (101)

Lecture 49 Tokenization (Practical)

Lecture 50 Tokenization (Coding)

Lecture 51 Bible Vector DB - The Full Picture

Lecture 52 Bible Vector DB - Data Prep (Coding)

Lecture 53 Bible Vector DB - Database Handling (Coding)

Lecture 54 Exercise: Movies Vector DB

Lecture 55 Solution: Movies Vector DB - Data Prep (Coding)

Lecture 56 Solution: Movies Vector DB - DB-Setup (Coding)

Lecture 57 Solution: Movies Vector DB - Query Function (Coding)

Lecture 58 Multimodal Vector DB (101)

Lecture 59 Multimodal Vector DB: Setup (Coding)

Lecture 60 Multimodal Vector DB: Query (Coding)

Section 6: OpenAI API

Lecture 61 Section Overview

Lecture 62 ChatGPT (101)

Lecture 63 OpenAI API (101)

Lecture 64 Get your API Key (Coding)

Lecture 65 Python Package (101)

Lecture 66 Python Package (Coding)

Lecture 67 Rest APIs (101)

Lecture 68 OpenAI WebUI (Coding)

Lecture 69 Cost (101)

Section 7: Prompt Engineering

Lecture 70 Prompt Engineering (101)

Lecture 71 Clear Instructions (Coding)

Lecture 72 Personas (Coding)

Lecture 73 Delimiters (Coding)

Lecture 74 Divide into sub-tasks (Coding)

Lecture 75 Provide Examples (Coding)

Lecture 76 Control Output (Coding)

Section 8: Retrieval-Augmented Generation (RAG)

Lecture 77 RAG (101)

Lecture 78 RAG Coding - The Final Result

Lecture 79 RAG: Handling Vector DB (Coding)

Lecture 80 RAG: Handling LLM (Coding)

Lecture 81 RAG: Putting all together (Coding)

Section 9: Capstone Project "Chatbot"

Lecture 82 Webapp Climate Change Chatbot (101)

Lecture 83 Webapp Climate Change Chatbot: Data Prep (Coding)

Lecture 84 Webapp Climate Change Chatbot: Vector DB (Coding)

Lecture 85 Webapp Climate Change Chatbot: RAG (Coding)

Lecture 86 Webapp Climate Change Chatbot: Webapp (Coding)

Section 10: Open Source LLMs

Lecture 87 Open Source LLMs (101)

Lecture 88 Open Source LLMs (Coding)

Section 11: Data Augmentation

Lecture 89 Data Augmentation (101)

Lecture 90 Data Augmentation: Back-Translation (Coding)

Lecture 91 Data Augmentation: Replacement with Synonyms (Coding)

Lecture 92 Data Augmentation: Random Cropping (Coding)

Lecture 93 Data Augmentation: Contextual Augmentation (Coding)

Lecture 94 Data Augmentation: Word Embeddings (Coding)

Lecture 95 Data Augmentation: Fill-Mask (Coding)

Developers who want to apply NLP-models