Google, the popular search engine and AI giant is now offering a free course for Generative AI. They offer 10 separate courses to clear your concept of Generative AI technology.
What is generative AI?
Nowadays, generative AI is changing the world. It forces us to rethink the way we work. These kinds of models or platforms can do all manual tasks in seconds. AI-powered companies like Google, Microsoft all are using Generative AI models in various of their products.
Generative AI is a type of artificial intelligence that focuses on creating new content on its own. The architecture of these generative models is designed in a way that you can ask them anything, and they will respond using their own knowledge and voice. Not copping from anywhere.
A good example of a Generative AI application is ChatGPT. You can ask anything to it and it will give you an answer in its own word.
The knowledge of these models depends on the volume of the training data used to train that Generative model.
Google Generative AI Learning Path
Google divided the entire course into 10 parts. Before going directly to those modules let’s first understand what those modules contain. After completion of each module of this course, you will earn a completion badge.
1. Introduction to Generative AI
This is the first course that provides an introduction to Generative AI. This 45-minute, short video explains the purpose, and usage of generative ai and how it differs from traditional machine learning approaches.
This video also guides you about different Google Tools which you can use to develop your own Gen AI applications.
2. Introduction to Large Language Models
The estimated duration of this course is also approximately 45 minutes. This module is to make you familiar with large language models (LLMs).
In this video, they will teach you about the purpose, use cases, and technique of prompt tuning to improve LLM performance. They will also show you what kind of Google tools you can use to work with language models (LLMs).
3. Introduction to Responsible AI
In this section, they will guide you about responsible AI. They will also show you some Google products, where they are using responsible AI.
This module covers the fundamentals and significance of responsible AI, why it is crucial, and highlights Google’s 7 AI principles.
4. Introduction to Image Generation
This course provides an introduction to diffusion models. In the last few years, diffusion models gained attention for their success in image generation.
Diffusion models are inspired by principles from physics, particularly a branch called thermodynamics. In thermodynamics, diffusion refers to the process of particles or molecules spreading out from an area of high concentration to areas of lower concentration.
Just like in physics, diffusion models in machine learning aim to capture the gradual spreading or transformation of information. They achieve this by iteratively updating a probability distribution over time. This technique allows the model to generate realistic outputs.
In this module of the Generative AI course, They will teach you the theory behind diffusion models and how to train and deploy them on Google Vertex AI platform.
5. Encoder-Decoder Architecture
This part of the course provides a summary of the encoder-decoder architecture. Encoder-decoder is a powerful and widely used machine learning approach for NLP tasks like machine translation, text summarization, question answering, etc.
In this section, they will teach you main components of the encoder-decoder architecture and how to train and use these models.
There is a practical lab session where you will code to build a poetry generator using a simple encoder-decoder architecture with TensorFlow.
6. Attention Mechanism
Attention mechanism is a powerful idea in the field of machine learning. This technique helps a neural network to focus specific parts of an input sequence.
In this 45-minute video course, you will understand how attention works and how it can enhance the performance of various machine learning tasks like: machine translation, text summarization, and question answering.
7. Transformer Models and BERT Model
In my last tutorial, I showed you how you can make a Question answering system with BERT model. Now BERT model is developed using Transformer architecture.
In this course, they will teach different components of Transformer architecture. After completion of this module, you will have a clear understanding about how different components of transformer architecture like self-attention mechanism are used to build the BERT model.
In this 45-minute video tutorial they will also discuss about different kinds of tasks BERT model can handle such as: Text Classification, Question Answering, Named Entity Recognition, etc.
8. Create Image Captioning Models
Image captioning is a computer vision task where you give an image to the model and the model generates a descriptive caption or sentence that accurately represents the content of an image.
In this course, you will learn how to train and evaluate an image captioning model using deep learning techniques like encoder-decoder. At the end of this course, you will be able to create your own image captioning model.
9. Introduction to Generative AI Studio
Generative AI Studio is a product of Google which you can access through Vertext AI. It helps you to customize your generative ai model so that it can be fitted to your application.
In this course, you will learn about different features, and options of Generative AI Studio and how it can be used in your project. At the end of this module, they will also give you a quiz to test your understanding.
10. Generative AI Explorer – Vertex AI
In this section of the course, you will get a set of hands-on labs that teach you how to utilize Generative AI on Google Cloud. These labs will guide you to use various models like text-bison, chat-bison, and textembedding-gecko available in the Vertex AI PaLM API family.
These labs will also teach you about how you can use Generative AI to build different solutions like: text classification, text summarization, text extraction, and more.
Register free Google Generative AI Course
Google Generative AI Course is available now in Cloudskillsboost portal. To avail this course you need to sign up in that portal.
After signing up go to this link to register Google Generative AI Course for free.
Generative AI in Real-world
Nowadays Generative AI is used in various fields like:
- Data Augmentation: It can be used to generate realistic synthetic data for model training
- Image Generation: Generative AI models can generate realistic images which can compete with photographs or artwork. One popular application is DALL-E
- Text Generation: Generative AI can generate human-like text, including stories, poems, or even news articles. A popular example of this kind of application is ChatGPT, BARD, etc.
- Music Composition: It can assist musicians in creating melodies, harmonies, and even entire compositions.
- Style Transfer: Generative AI can convert the style of one image or artwork to another
Career Opportunity of Generative AI
Generative AI is a tough field if you want to build any model from scratch. But after doing this course you will get to know how to use pre-trained models to solve your task easily with Generative AI.
After completing Google Generative AI course, you will get a certificate that you can add to your resume and share with your LinkedIn connection.
Nowadays lots of company implementing Generative AI with deep learning. Conventional Machine learning is past now. This course will help you to get a job in those companies.
Obtaining a certificate in Generative AI can also be beneficial for showcasing your skills and expertise to your current employer. It may provide an opportunity for career growth within your organization.
Hi there, I’m Anindya Naskar, Data Science Engineer. I created this website to show you what I believe is the best possible way to get your start in the field of Data Science.