Applied Generative AI and Natural Language Processing

Applied Generative AI and Natural Language Processing, available at $54.99, has an average rating of 4.61, with 121 lectures, based on 278 reviews, and has 14192 subscribers.

You will learn about 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 This course is ideal for individuals who are Developers who want to apply NLP-models It is particularly useful for Developers who want to apply NLP-models.

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Summary

Title: Applied Generative AI and Natural Language Processing

Price: $54.99

Average Rating: 4.61

Number of Lectures: 121

Number of Published Lectures: 121

Number of Curriculum Items: 121

Number of Published Curriculum Objects: 121

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

Developers who want to apply NLP-models

Target Audiences

Developers who want to apply NLP-models

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

Miscellanious

Claude 3

Tools and LLM-Function

Course Curriculum

Chapter 1: Course-Introduction

Lecture 1: Course Scope (101)

Lecture 2: Who am I?

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)

Chapter 2: NLP-Introduction

Lecture 1: Section Overview

Lecture 2: NLP (101)

Lecture 3: Word Embeddings (101)

Lecture 4: Sentiment OHE Coding Intro

Lecture 5: Sentiment OHE (Coding)

Lecture 6: Word Embeddings with NN (101)

Lecture 7: GloVe: Get Word Embedding (Coding)

Lecture 8: GloVe: Find closest words (Coding)

Lecture 9: GloVe: Word Analogy (Coding)

Lecture 10: GloVe: Word Cluster (101)

Lecture 11: GloVe Word (Coding)

Lecture 12: Sentiment with Embedding (101)

Lecture 13: Sentiment with Embedding (Coding)

Lecture 14: Transformers (101)

Chapter 3: Apply Huggingface for Pre-Trained Models

Lecture 1: Section Overview

Lecture 2: Huggingface (101)

Lecture 3: Pipelines: General Use (101)

Lecture 4: Text Classification (101)

Lecture 5: Pipelines: General Use (Coding)

Lecture 6: Named Entity Recognition (101)

Lecture 7: Named Entity Recognition (Coding)

Lecture 8: Question Answering (101)

Lecture 9: Question Answering (Coding)

Lecture 10: Text Summarization (101)

Lecture 11: Text Summarization (Coding)

Lecture 12: Translation (101)

Lecture 13: Translation (Coding)

Lecture 14: Fill-Mask (101)

Lecture 15: Fill-Mask (Coding)

Lecture 16: Zero-Shot Text Classification (101)

Lecture 17: Zero-Shot Text Classification (Coding)

Chapter 4: Model Finetuning

Lecture 1: Section Overview

Lecture 2: Simple Model (101)

Lecture 3: Exploratory Data Analysis (Coding)

Lecture 4: Simple Model (Coding)

Lecture 5: Finetuning Model (101)

Lecture 6: Huggingface Trainer (101)

Lecture 7: Finetuning Model (Coding)

Lecture 8: Saving Model to huggingface / Loading Model (Coding)

Chapter 5: Vector Databases

Lecture 1: Vector Databases (101)

Lecture 2: Tokenization (101)

Lecture 3: Tokenization (Practical)

Lecture 4: Tokenization (Coding)

Lecture 5: Bible Vector DB – The Full Picture

Lecture 6: Bible Vector DB – Data Prep (Coding)

Lecture 7: Bible Vector DB – Database Handling (Coding)

Lecture 8: Exercise: Movies Vector DB

Lecture 9: Solution: Movies Vector DB – Data Prep (Coding)

Lecture 10: Solution: Movies Vector DB – DB-Setup (Coding)

Lecture 11: Solution: Movies Vector DB – Query Function (Coding)

Lecture 12: Multimodal Vector DB (101)

Lecture 13: Multimodal Vector DB: Setup (Coding)

Lecture 14: Multimodal Vector DB: Query (Coding)

Chapter 6: OpenAI API

Lecture 1: Section Overview

Lecture 2: ChatGPT (101)

Lecture 3: OpenAI API (101)

Lecture 4: Get your API Key (Coding)

Lecture 5: Python Package (101)

Lecture 6: Python Package (Coding)

Lecture 7: Rest APIs (101)

Lecture 8: OpenAI WebUI (Coding)

Lecture 9: Cost (101)

Chapter 7: Prompt Engineering

Lecture 1: Prompt Engineering (101)

Lecture 2: Clear Instructions (Coding)

Lecture 3: Personas (Coding)

Lecture 4: Delimiters (Coding)

Lecture 5: Divide into sub-tasks (Coding)

Lecture 6: Provide Examples (Coding)

Lecture 7: Control Output (Coding)

Chapter 8: Advanced Prompt Engineering

Lecture 1: Advanced Prompt Engineering (101)

Lecture 2: Few-Shot Prompting (101)

Lecture 3: Chain-of-Thought (101)

Lecture 4: Chain-of-Thought (Example)

Lecture 5: Chain-of-Thought (Coding)

Lecture 6: Self-Consistency Chain-of-Thought (101)

Lecture 7: Self-Consistency Chain-of-Thought (Example)

Lecture 8: Self-Consistency Chain-of-Thought (Coding)

Lecture 9: Prompt Chaining (101)

Lecture 10: Prompt Chaining (Example)

Lecture 11: Reflection (101)

Lecture 12: Tree-of-Thought (101)

Lecture 13: Self-Feedback (101)

Lecture 14: Self-Feedback (Example)

Lecture 15: Self-Feedback (Coding)

Lecture 16: Self-Critique (101)

Instructors

Bert Gollnick
Data Scientist

Rating Distribution

1 stars: 5 votes

2 stars: 2 votes

3 stars: 20 votes

4 stars: 56 votes

5 stars: 195 votes

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