Google recently released a Generative AI learning path with courses covering topics such as Introduction to Generative AI, Large Language Models, Image Generation, and more.
The best part is that some of the courses have no prerequisites and are completely free, so even individuals with no programming experience can benefit from them.
Here's everything you need to know about these artificial intelligence courses.
Who is this course intended for?
This course is for anyone who wants to learn about Generative AI products, Large Language Models, and how to deploy Generative AI solutions.
However, of the ten courses given by Google, about five require some experience of Python and Machine Learning. But don't worry; in the following part, I'll go over each course in further detail and point out which courses have no prerequisites.
By the way, completing a course earns you a wonderful badge like the one below.
What is the scope of Google's Generative AI learning path?
Google's Generative AI learning route walks you through a handpicked range of generative AI products and technologies.
The following is a rundown of the 10 courses in the learning path.
No Prerequisites required
Passion, desire, a PC/Macbook and an internet connection are the only prerequisites.
- Introduction to Generative AI Studio: Explains what Generative AI Studio is, its features and settings, and how to use it.
- Introduction to Large Language Models (LLMs): Explains what LLMs are, as well as use cases and prompt engineering on LLMs.
- Introduction to Generative AI: Describes what Generative AI is, how it is utilized, and how it differs from typical machine learning methods.
- Introduction to Responsible AI: Explains what responsible AI is, why it is important, and how Google incorporates responsible AI into its products.
Prerequisites required
Knowledge in Python programming, Machine learning, and Deep learning
- Transformer Models and the BERT Model: This course describes the basic components of the transformer architecture and how they are utilized to construct the BERT model.
- Create Image Captioning Models: Shows you how to use deep learning to create an image captioning model.
- Encoder-Decoder Architecture: This course describes the key components of the encoder-decoder architecture, as well as how to train and service these models.
- Attention Mechanism: Explains how attention works and how it might help with machine learning tasks like translation, summarization, and question answering.
- Introduction to Image Generation: Explains the theory underlying diffusion models and how to train and deploy them using Vertex AI.
How to Enroll in the Course
The Google Cloud platform hosts this learning path. To enrol in any of the courses in the Generative AI Learning Path, please click here.
Keep in mind that this isn't the only free course offered on the Google Cloud platform; there are also Data Engineer Learning Paths, Data Analyst Learning Paths, and so on. Click here to view the entire Google Cloud Skill Boost catalogue.
Additional Free AI Courses
There are numerous free AI courses available on the internet. One of my favourite courses is OpenAI and Andrew Ng's ChatGPT Prompt Engineering Course. This will be included in my next article.
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