Google has introduced a learning path for generative AI that includes lessons on subjects like introduction to generative AI, large language models, image generation, etc.

The greatest part is that some of the courses are free and don't need any prior knowledge, allowing anyone—even those without any programming experience—to benefit from them.

Here is all the information you want about these AI courses.

Who should take this course?

This course is recommended for anybody who is interested in learning more about Large Language Models, Generative AI products, and how to implement Generative AI solutions.

Nevertheless, out of the 10 courses that Google offers, around 5 of them call for some familiarity with Python and machine learning. Don't worry, however; I'll go into greater detail about each course and identify those that don't need any prerequisites in the part after this one.

By the way, you will get a wonderful badge similar to the one below after finishing a course.

What is covered by Google's learning route for generative AI?

You may navigate a selected selection of articles about generative AI products and technologies using Google's Generative AI learning route.

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The 10 courses that are part of the learning route are summarised here.

There are no prerequisites for the five courses listed below.

An introduction to Generative AI describes the concept, its applications, and how it differs from more conventional machine learning techniques.

Large Language Models (LLM) Introduction: Describes LLMs, their use cases, and prompt engineering on LLMs. 

Introduction to Responsible AI: Describes responsible AI, its significance, and how Google incorporates it into its products.

Introduces Generative AI Studio and explains its purpose, features, and functionalities. It also shows you how to utilise it.

The remaining courses, on the other hand, call for proficiency in Python programming, machine learning, and deep learning.

Introduction to Image Generation: Explains how to train and use diffusion models on Vertex AI, as well as the theory behind them.

Explains the key elements of the encoder-decoder architecture as well as how to train and use these models.

Describes the attention mechanism and how it affects the efficiency of several machine learning tasks, including question answering, translation, and summarization.

Explains the fundamental elements of the transformer architecture and how the BERT model is constructed using it.

develop Image Captioning Models: This tutorial shows you how to use deep learning to develop an image captioning model.

How to enrol in the programme

The Google Cloud platform is used to host this learning route.To enrol in any of the courses on the Generative AI Learning Path, click here.

Remember that there are additional interesting free courses on the Google Cloud platform, such the Data Engineer Learning Path, Data Analyst Learning Path, etc.To see the whole Google Cloud Skill Boost catalogue, go here.