Mastering Generative AI- From Python to NLP, GPT 4 LLMs

Mastering Generative AI: From Python to NLP, GPT 4 & LLMs, available at $39.99, has an average rating of 3.9, with 87 lectures, based on 17 reviews, and has 92 subscribers.

You will learn about Grasp Generative AI Basics: Understand the principles, history, and workings of generative AI, including large language models. Deploy AI Applications: Learn to design, build, and deploy generative AI applications using cloud-based solutions. Master Prompt Engineering: Develop advanced skills in crafting effective AI prompts to guide model responses accurately. Navigate AI Ethics and Applications: Recognize the ethical considerations and practical applications of generative AI, including risk mitigation Grasp NLP Fundamentals: Understand the basics of computational linguistics and Pythons role in NLP. Acquire Text Processing Skills: Master techniques for parsing and manipulating text using Python. Implement NLP Algorithms: Apply advanced algorithms for sentiment analysis, topic modeling, and text classification. Utilize GPT-4: Leverage GPT-4 for workflow automation and enhanced text analysis. Create Chatbots and Analyze Sentiment: Build chatbots and tools for detecting sentiment in text. Visualize Text Data: Employ visualization techniques to reveal insights from text data. Apply NLP in Real-World Applications: Use NLP skills in diverse scenarios, from customer service to social media analysis. Integrate Machine Learning: Combine machine learning with NLP for detailed data analysis. Understand Ethical NLP Use: Recognize ethical considerations in NLP application. Prepare Datasets for NLP: Efficiently prepare and process data for NLP model training. Extract Open-Source Data: Extract and use open-source data effectively in NLP projects. This course is ideal for individuals who are Web Developers or Software Developers or Programmers or Anyone interested in machine learning and Python or Anyone interested in AGI It is particularly useful for Web Developers or Software Developers or Programmers or Anyone interested in machine learning and Python or Anyone interested in AGI.

Enroll now: Mastering Generative AI: From Python to NLP, GPT 4 & LLMs

Summary

Title: Mastering Generative AI: From Python to NLP, GPT 4 & LLMs

Price: $39.99

Average Rating: 3.9

Number of Lectures: 87

Number of Published Lectures: 85

Number of Curriculum Items: 87

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Grasp Generative AI Basics: Understand the principles, history, and workings of generative AI, including large language models.

Deploy AI Applications: Learn to design, build, and deploy generative AI applications using cloud-based solutions.

Master Prompt Engineering: Develop advanced skills in crafting effective AI prompts to guide model responses accurately.

Navigate AI Ethics and Applications: Recognize the ethical considerations and practical applications of generative AI, including risk mitigation

Grasp NLP Fundamentals: Understand the basics of computational linguistics and Pythons role in NLP.

Acquire Text Processing Skills: Master techniques for parsing and manipulating text using Python.

Implement NLP Algorithms: Apply advanced algorithms for sentiment analysis, topic modeling, and text classification.

Utilize GPT-4: Leverage GPT-4 for workflow automation and enhanced text analysis.

Create Chatbots and Analyze Sentiment: Build chatbots and tools for detecting sentiment in text.

Visualize Text Data: Employ visualization techniques to reveal insights from text data.

Apply NLP in Real-World Applications: Use NLP skills in diverse scenarios, from customer service to social media analysis.

Integrate Machine Learning: Combine machine learning with NLP for detailed data analysis.

Understand Ethical NLP Use: Recognize ethical considerations in NLP application.

Prepare Datasets for NLP: Efficiently prepare and process data for NLP model training.

Extract Open-Source Data: Extract and use open-source data effectively in NLP projects.

Who Should Attend

Web Developers

Software Developers

Programmers

Anyone interested in machine learning and Python

Anyone interested in AGI

Target Audiences

Web Developers

Software Developers

Programmers

Anyone interested in machine learning and Python

Anyone interested in AGI

Notice: Effective April 15, 2024, this course has been thoroughly updated. Rest assured, it will consistently be refreshed to ensure its ongoing relevance and effectiveness.

Unlock the Future of AI: Master Generative AI & NLP with Python. Dive into the Revolution: Generative AI & NLP Mastery

Embark on a groundbreaking journey through the realms of Artificial Intelligence with our two-part comprehensive course, designed to transform beginners into skilled practitioners capable of shaping the future of technology. Whether you’re looking to supercharge your career, innovate in your current role, or simply dive deep into AI, this course is your pathway to mastering Generative AI and Natural Language Processing (NLP) with Python.

Part 1: Generative AI Unleashed

Begin your adventure in the fascinating world of Generative AI and Large Language Models (LLMs), where innovation meets creativity. This section is meticulously designed to introduce you to the cutting-edge of AI without requiring any prior experience.

Discover the Essence of Generative AI: Learn the foundations, history, and evolution of Generative AI and how it’s revolutionizing industries.

Hands-On Learning: Engage with leading platforms like GitHub Copilot, Qdrant, and OpenAI, applying your newfound knowledge in practical scenarios.

Master Prompt Engineering: Delve into the art of crafting effective prompts, from basic to advanced techniques, and learn how to refine AI-generated outputs for optimal results.

Navigate Real-World Applications: Explore the landscape of generative AI applications, from API-based to multi-model solutions, and gain insights into deploying AI applications with cloud solutions like Azure.

Part 2: The Power of NLP & Python

Elevate your skills further with an in-depth exploration of Natural Language Processing (NLP) using Python, the core of modern computational linguistics and AI-driven human-computer interaction.

Begin with NLP Basics: Start with an immersive introduction to NLP, diving into methodologies and engaging in Python projects for dataset preparation.

Explore Advanced Concepts: Tackle sentiment analysis, neural network-based text generation, and innovative data visualization methods.

Experience GPT-4 Innovation: Discover the enhancements in GPT-4 over its predecessors, unlocking new levels of efficiency and precision in data analysis.

Bridge the Human-Computer Divide: Learn about the parallel processing capabilities of humans and computers, and develop sophisticated chatbots and text data visualization techniques.

Why Choose This Course?

By the conclusion of this comprehensive course, you will possess a profound understanding of both Generative AI and NLP, equipped with practical experience and ready to apply your skills in diverse professional settings. This course is not just an educational journey; it’s a launchpad into the forefront of AI innovation, offering you the tools and knowledge to experiment confidently and responsibly in this rapidly evolving field.

Enroll now to unlock the transformative power of AI, mastering Generative AI and NLP with Python, and begin shaping the future of technology today.

Course Curriculum

Chapter 1: Welcome

Lecture 1: Welcome to the Course: Navigating the Future with Generative AI

Chapter 2: Foundations of Generative AI

Lecture 1: A Journey Through Time: The Evolution of Artificial Intelligence

Lecture 2: Unveiling the Magic: How Large Language Models Transform Applications

Lecture 3: The Birth of Giants: Constructing Large Language Models

Lecture 4: Encapsulating Wisdom: Key Takeaways and Reflections

Lecture 5: Exploring Large Language Models (LLMs): Operation and Impact

Lecture 6: Deep Dive: The Fundamentals of Large Language Models Explained

Lecture 7: The Power and Peril of Large Language Models: A Dual Perspective

Lecture 8: Safeguarding the Future: Strategies to Minimize Risks of Large Language Models

Lecture 9: The Pillars of Modern AI: Understanding Foundation Models

Lecture 10: Consolidating Insights: A Summary of Key Learnings

Lecture 11: Navigating the Ecosystem of Generative AI

Lecture 12: The Dawn of New Intelligence: Exploring the Impact of OpenAI and ChatGPT

Lecture 13: Democratizing AI: The Role of Hugging Face and the Open Source Ecosystem

Lecture 14: Empowering Users: The Advantages of Deploying Local AI Models

Lecture 15: Elevating AI Accessibility: An Overview of Cloud-Based AI Solutions

Lecture 16: Ecosystem Summary

Chapter 3: Mastering Prompt Engineering

Lecture 1: The Art of Prompt Engineering

Lecture 2: Introduction to Prompt Engineering

Lecture 3: Mastery in Zero, One, and Few-Shot Prompting

Lecture 4: Contextual Prompting Basics

Lecture 5: Enhancing Prompts with Examples

Lecture 6: Prompting Summary

Lecture 7: Crafting Effective AI Interactions

Lecture 8: Defining Tone and Persona in Prompts

Lecture 9: Building on Context and Refining Responses

Lecture 10: The Power of Feedback in Prompt Refinement

Lecture 11: Understanding AIs Limitations for Better Prompts

Lecture 12: Interaction Summary

Lecture 13: Advanced Prompting Strategies for Precision and Insight

Lecture 14: Learn and Understand Limitations

Lecture 15: Tackling Limitations Through Task Breakdown

Lecture 16: Enhancing AI Output with Chain of Thought Prompting

Lecture 17: Exploring Various Prompt Techniques for Optimal Results

Lecture 18: Strategies Summary

Chapter 4: Deploying Generative AI Applications

Lecture 1: Building Generative AI Applications: A Comprehensive Guide

Lecture 2: Introduction to Generative AI Applications

Lecture 3: Designing API-Based Applications

Lecture 4: Embedding Models in Applications

Lecture 5: The Multi-Model Approach: Integration and Challenges

Lecture 6: Learn About Challenges And Highglights

Lecture 7: Application Building Summary

Lecture 8: Enhancing AI with Retrieval-Augmented Generation (RAG)

Lecture 9: Fundamentals of Retrieval-Augmented Generation

Lecture 10: Managing Data for Effective RAG Implementation

Lecture 11: The Role of Embeddings and Search in RAG

Lecture 12: Integrating RAG with LLMs for Advanced Applications

Lecture 13: RAG Summary

Lecture 14: Deploying AI: From Concept to Cloud

Lecture 15: Overview of AI Application Deployment

Lecture 16: Deployment Essentials: An Overview

Lecture 17: Configuring Azure for AI Deployment

Lecture 18: Utilizing Azure Cloud for Seamless Deployment

Lecture 19: Deployment Summary

Lecture 20: Wrapping Up: The Journey Ahead in Generative AI

Chapter 5: Part 2: The Power of NLP & Python

Lecture 1: Introduction

Chapter 6: Getting Started with This Part

Lecture 1: Introduction

Lecture 2: Learn How to Work with Unicode

Chapter 7: Learn to Convert Text To Symbols

Lecture 1: Introduction

Lecture 2: Text To Symbols – Splitting Sentences

Lecture 3: Text To Symbols – Filtering Stop Words

Chapter 8: Python – Going Subsymbolic

Lecture 1: Introduction

Lecture 2: Learn About Word Vectors

Lecture 3: Learn About Google Word Vectors

Lecture 4: Subsymbolic – Learn Word Vectors

Chapter 9: Learn About the Structure of Text

Lecture 1: Introduction

Lecture 2: Learn and Understand Sentence Head

Lecture 3: Learn and Understand Named Entities

Chapter 10: Machine Learning – Determining How the Writer Feels

Lecture 1: Introduction

Lecture 2: Machine Learning – Sentiment In VADER

Chapter 11: Machine Learning – Making Decisions

Lecture 1: Introduction

Lecture 2: Classification with TextBlob

Lecture 3: Classification with scikit-learn

Chapter 12: Machine Learning – Identifying Discussed Topics

Lecture 1: Introduction

Lecture 2: Learn About LDA Gensim

Lecture 3: Learn About LDA pyLDAvis

Chapter 13: Machine Learning – Toward Machine Reading

Lecture 1: Introduction

Lecture 2: Learn About pyspotlight

Lecture 3: Learn About FRED

Chapter 14: Update: NLP & GPT

Lecture 1: Introduction

Lecture 2: Advanced concepts and applications

Lecture 3: Concepts, techniques, and applications

Chapter 15: Summary

Lecture 1: Course Summary

Lecture 2: Course Material & Source Code

Lecture 3: Thank You!

Instructors

John Wheeler
AI Expert and Educator in Generative AI Technologies

Alex Bennett
AI Innovator in Generative Technologies

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 1 votes

5 stars: 12 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!