AI is at its peak than ever. More students want to learn AI. Apart from University courses, several online courses are offered by top companies across the globe. Online courses offer flexibility for students that universities cannot provide without issues of credibility. 

As AI-powered apps and other technologies take the world by storm, the demand for tech workers with these specialized skills continues to rise. In this scenario, where skilling and reskilling are necessary, AI courses that provide basic, intermediate and advanced-level training play a significant role.  

Here, let us look at generative AI courses offered by Google. The course 'Generative AI Learning Path' guide the students through a curated collection of content on generative AI products and technologies, from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud.  

• 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 apps. This course is estimated to take approximately 45 minutes to complete. 

Click here to enroll in this course. 

• Introduction to Large Language Models: This introductory-level microlearning course 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 help you develop your own Gen AI apps. This course is estimated to take approximately 45 minutes to complete.

Click here to enroll in this course. 

• Introduction to Responsible AI: This is an introductory-level microlearning course to explain responsible AI, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles. 

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• Generative AI Fundamentals: Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. 

Click here to enroll in this course.

• Introduction to Image Generation: This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models have become popular in research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI. 

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• Encoder-Decoder Architecture: This course gives you a synopsis of the encoder-decoder architecture, a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you'll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning. 

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• Attention Mechanism: This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works and how it can improve the performance of various machine-learning tasks, including machine translation, text summarization, and question-answering. 

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• Transformer Models and BERT Model: This course introduces the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. 

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• Create Image Captioning Models: This course teaches you how to create an image captioning model using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your image captioning models and use them to generate captions for images. 

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• Introduction to Generative AI Studio: This course introduces Generative AI Studio, a product on Vertex AI that helps you prototype and customize generative AI models so you can use their capabilities in your applications. In this course, you learn what Generative AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, you will have a quiz to test your knowledge. 

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Users can access the course free of cost. After completing the course, Google Cloud will provide the student with a 'Completion Badge'.