The advent of Artificial Intelligence has made folks worried about their jobs, however, it is not all doom and gloom. As resilient human beings are, AI does give them a way to adopt and grow. Â
Generative AI is the most recent innovation and creativity introduced by Artificial Intelligence in its area, which is developing so rapidly. AI-based applications are changing industries and redefining the rules with which we engage technology and therefore create unprecedented demand for expertise in Generative AI.
Google has come forward to launch a comprehensive and wide-ranging series of free courses to equip aspiring professionals in AI with foundational skills so that they can thrive in such an environment.
No matter your age or level of education, these courses are for everyone in worldwide, and all it takes is one click to get started.
What is AI?
Artificial intelligence is the field of computer science that covers the creation of machines that perform tasks that are thought of as requiring human intelligence. The topic is pretty broad and also multidisciplinary, even including machine learning and even data science. Applications come in all fields.
Google Free AI Courses
Introduction to Generative AI
This micro-learning course teaches the basic knowledge of Generative AI, its uses and how Google tools can be used to develop Gen AI apps.
Introduction Large Language Models
This course covers large language models, applications, and techniques such as prompt tuning, which improve performance on LLM. It also introduces Google’s tools to build Gen AI apps.
Introduction to Responsible AI
Responsible AI is explained in this course and rationales for why responsible AI is important and how Google approaches the implementation of responsible AI principles through Google’s 7 AI principles.
Beginner: Generative AI Learning Path
A learning path that covers the full breadth of generative AI concepts, from LLM fundamentals to responsible AI.
Introduction to Image Generation
The course explains diffusion models, inspired by physics that has proven effective in image generation. It also covers model training and deployment on Vertex AI.
Encoder-Decoder Architecture
Overview of the encoder-decoder architecture, which is used in many translation and text summarization tasks. The course has a TensorFlow lab where students will have hands-on practice building an encoder-decoder model.
Attention Mechanism
This course covers attention mechanisms that allow neural networks to focus on specific parts of an input sequence for better translation and text summarization tasks.
Transformer Models and BERT Model
The course explains the Transformer architecture and BERT with a particular focus on self-attention mechanisms and applications such as text classification and natural language inference.
Create Image Captioning Models
Learn how an image captioning model would be implemented using deep learning with encoder-decoder component and how models are put to the test for performing caption generation.
