Introduction Courses

These are crash courses that will teach the basics of AI, they are very interactive.

They were designed for a non-technical audience and no prior knowledge of AI is required. 🌍🚀👫 

• Introduction to Generative AI Learning Path - Google

• Generative AI for Everyone - DeepLearning

• Intro to Large Language Models, by Andrej Karpathy 

• Generative AI for Beginners - Microsoft

Intermediate Courses

• Low-Code Machine Learning - AWS

• Foundations of Prompt Engineering - AWS

Technical Courses

💡🛠️👩‍💻 = Coding required

• NLP Course - Hugging Face

• LLM University - Cohere

• A 12 Lesson course teaching everything you need to know to start building Generative AI applications

GOOGLE

Introduction to Generative AI Learning Path

🌍🚀👫 

This learning path provides an overview of generative AI concepts, from the fundamentals of large language models to responsible AI principles.

• 🤖 Intro to Generative AI

• 🤖 Intro to Large Language Models

• 🤖 Intro to Responsible AI

• 🤖 Generative AI Fundamentals

• 🤖 Responsible AI: Applying AI Principles with Google Cloud

Go to the course: Intro Gen AI

DEEP LEARNING

AI for Everyone - DeepLearning
( Andrew Ng Initiative )

Stanford professor and AI pioneer Andrew Ng recently announced an exciting new online course focused on generative AI.

Titled "Generative AI: The Latest Breakthroughs and Ethical Considerations", this course aims to make cutting-edge generative AI concepts accessible to a broad audience.

All public Courses 🌍🚀👫 

• 🤖 Generative AI for Everyone

Technical Courses 💡🛠️👩‍💻

• 🤖 ChatGPT Prompt Engineering for Developers 

• 🤖 Building Generative AI Applications with Gradio 

• 🤖 LLMops

• 🤖 …

HUGGING FACE

NLP Course

💡🛠️👩‍💻

This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It’s completely free and without ads.

What to expect?

Here is a brief overview of the course:

COHERE

LLM University by Cohere!

💡🛠️👩‍💻

In this course, you will learn everything about Large Language Models (LLMs), including:

• How do LLMs work?: Learn about their architecture and their moving pieces, including transformer models, embeddings, similarity, and attention mechanisms.

• What are LLMs useful for?: Learn about many real-world applications of LLMs, including:

  • Semantic search

  • Text generation

  • Text classification

  • Analyzing text using embeddings

• How can I use LLMs to build and deploy my apps?: Learn how to use LLMs to build applications. This course will teach you:

  • How to use Cohere's endpoints: Classify, Generate, and Embed.

  • How to build apps, including semantic search models, text generators, etc.

  • (Coming soon...) How to deploy these apps on many platforms

MICROSOFT

Generative AI for Beginners

🌍🚀👫 

In this module you'll explore the way in which large language models (LLMs) enable AI applications and services to generate original content based on natural language input. You’ll also learn how generative AI enables the creation of AI-powered copilots that can assist humans in creative tasks.

Learning objectives

By the end of this module, you'll be able to:

• Understand generative AI's place in the development of artificial intelligence

• Understand large language models and their role in intelligent applications

• Describe how Azure OpenAI supports intelligent application creation

• Describe examples of copilots and good prompts

MICROSOFT

A 12 Lesson course teaching everything you need to know to start building Generative AI applications

💡🛠️👩‍💻

Learn the fundamentals of building Generative AI applications with our 12-lesson comprehensive course by Microsoft Cloud Advocates. Each lesson covers a key aspect of Generative AI principles and application development.

Throughout this course, you will be building your own Generative AI startup so you can get an understanding of what it takes to launch your ideas

ANDREJ KARPATHY

Intro to Large Language Models, by Andrej Karpathy 

Speaker: Andrej Karpathy
Context: Recently uploaded 1-hour informal talk on his YouTube channel
Focus: Non-technical introduction to large language models (LLMs)

Key topics covered:

• LLM training and inference

• Jailbreaking LLMs

• Fine-tuning techniques

• The concept of an emerging "LLM OS"

• LLM security challenges

Takeaway: It is likely one of the best current overviews of LLMs aimed at a general audience, providing an accessible yet comprehensive introduction without requiring technical prerequisites. Karpathy's first-hand expertise on large models adds further value for viewers interested in learning from a leader in the field.

AWS

Low-Code Machine Learning on AWS

With Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot, data and research analysts can prepare data, train, and deploy machine learning (ML) models with minimal coding. You will learn to build ML models for tabular and time series data without deep knowledge of ML. You will also review the best practices for using SageMaker Data Wrangler and SageMaker Autopilot.

After completing this course, you will be able to build ML models to support proofs of concept (POCs). You will also be able to assist data scientists with potential ML model candidates to solve business problems.

• Course level: Intermediate

• Duration: 4 hours

• Course outline

Module 1: Introduction to Machine Learning

ML Introduction

• ML Basics

• Problems ML Can Solve

• ML Life Cycle

• Challenges in Processing Data and Deriving Insights

• Knowledge Check

Model Building and Evaluation Metrics

• Introduction to Model Building

• Applying Evaluation Metrics to Select a Model

• Building an ML Model

Wrap Up

• Knowledge Check

• Conclusion

Module 2: Exploratory Data Analysis and Data Preparation

Introduction to SageMaker Data Wrangler

• SageMaker Data Wrangler

• Data Analysis

Data Preparation

• Quick Model

• Transforming Data

• Developing and Scaling Data Transformations

Wrap Up

• Knowledge Check

• Conclusion

Module 3: Deep Dive on Amazon SageMaker Autopilot

• Introduction to SageMaker Autopilot

• Datasets, Problem Types, and Training Modes

• Validation and Metrics

• Automatic Model Deployment

Wrap Up

• Knowledge Check

• Conclusion

Module 4: Operational Best Practices

Best Practices for SageMaker Data Wrangler

• Environmental Optimization

• Cost Optimization

• Data Optimization

• Security Optimization

Best Practices for SageMaker Autopilot

• Best Practices and Recommendations

AWS

Foundations of Prompt Engineering

In this course, you will learn the principles, techniques, and the best practices for designing effective prompts. This course introduces the basics of prompt engineering, and progresses to advanced prompt techniques. You will also learn how to guard against prompt misuse and how to mitigate bias when interacting with FMs.

• Course level: Intermediate

• Duration: 4 hours

Activities

This course includes eLearning interactions.

Course objectives

In this course, you will learn to:

• Define prompt engineering and apply general best practices when interacting with FMs

• Identify the basic types of prompt techniques, including zero-shot and few-shot learning

• Apply advanced prompt techniques when necessary for your use case

• Identify which prompt-techniques are best-suited for specific models

• Identify potential prompt misuses

• Analyze potential bias in FM responses and design prompts that mitigate that bias

Intended audience

This course is intended for:

• Prompt engineers, data scientists, and developers

Prerequisites

We recommend that attendees of this course have taken the following courses:

• Introduction to Generative AI - Art of the Possible (1 hour, digital course)

• Planning a Generative AI Project (1 hour, digital course)

• Amazon Bedrock Getting Started (1 hour, digital course)

Course outline

Introduction

• Introduction

• Basics of Foundation Models

• Fundamentals of Prompt Engineering

Prompt Types and Techniques

• Basic Prompt Techniques

• Advanced Prompt Techniques

• Model-Specific Prompt Techniques

• Addressing Prompt Misuses

• Mitigating Bias