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Prompt Engineering Community
Searching for online prompt engineering classes can feel like trying to find a hidden treasure without a map, especially if you're not sure where to start. But don't worry, it's actually way easier than you might think. Imagine the internet as a giant library filled with all sorts of courses and lessons that can teach you everything you need to know about prompt engineering, which is all about giving the right instructions to computers and artificial intelligence to get the best responses. The best part is, you don't need to be a computer genius or speak in complex codes to start learning. There are loads of beginner-friendly classes that use simple, straightforward language, making it easy for anyone to jump in and start learning. These classes cover the basics of how to communicate effectively with AI, ensuring that you get the results you want, whether it's for work, a project, or just for fun. And the coolest part? Many of these online courses offer interactive sessions and real-life examples, making learning not just informative but also really enjoyable. So, if you've ever been curious about how to make machines understand and follow your commands better, diving into online prompt engineering classes is a fantastic way to kickstart your journey. Just grab your computer, a comfy chair, and start exploring the vast world of prompt engineering online.
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The Best AI Tutorials & Guides
Big Data, Artificial Intelligence, and Ethics
Get Smarter About AI & ChatGPT
Google, Harvard, and others are providing free AI classes (no payment required).
Beginner: Introduction to Generative AI Learning Path
Introduction to Generative AI
This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI…>> start course
Introduction to Large Language Models
This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to... >> start course
Introduction to Responsible AI
This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles.>> start course
Prompt Design in Vertex AI
Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini...>> start course
Responsible AI: Applying AI Principles with Google Cloud
As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice.... >> start course
Microsoft AI
Artificial Intelligence for Beginners - A Curriculum
Explore the world of Artificial Intelligence (AI) with Microsoft's 12-week, 24-lesson curriculum! Dive into Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs enhance your learning. Perfect for beginners, this comprehensive guide, designed by experts, covers TensorFlow, PyTorch, and ethical AI principles. Start your AI journey today!"
In this curriculum, you will learn:
Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
Neural Architectures for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art.
Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.
What we will not cover in this curriculum:
Business cases of using AI in Business. Consider taking Introduction to AI for business users learning path on Microsoft Learn, or AI Business School, developed in cooperation with INSEAD.
Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum.
Practical AI applications built using Cognitive Services. For this, we recommend that you start with modules Microsoft Learn for vision, natural language processing, Generative AI with Azure OpenAI Service and others.
Specific ML Cloud Frameworks, such as Azure Machine Learning, Microsoft Fabric, or Azure Databricks. Consider using Build and operate machine learning solutions with Azure Machine Learning and Build and Operate Machine Learning Solutions with Azure Databricks learning paths.
Conversational AI and Chat Bots. There is a separate Create conversational AI solutions learning path, and you can also refer to this blog post for more detail.
Deep Mathematics behind deep learning. For this, we would recommend Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at https://www.deeplearningbook.org/.
For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure Learning Path.
HarvardX
Online courses from Harvard University
HarvardX: CS50's Introduction to Artificial Intelligence with Python
Learn to use machine learning in Python in this introductory course on artificial intelligence.
This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.