The following six free AI courses offer a structured pathway for beginners to start their journey into the world of artificial intelligence. Each course is designed to introduce fundamental concepts and practical tools in a concise and manageable format:
1. Introduction to Generative AI: This course provides an introductory overview of Generative AI, explaining what it is and how it differs from traditional machine learning methods. Participants will learn about the applications of Generative AI and explore tools developed by Google to create their own AI-driven applications. This microlearning module is perfect for those curious about how AI can generate content and innovate across various fields.
2. Introduction to Responsible AI: This course focuses on the ethical aspects of AI technology. It introduces learners to responsible AI and explains why it is crucial in developing AI systems. The course also covers Google’s seven principles of AI, guiding participants on how to implement AI responsibly in their projects. This is crucial for ensuring AI technology is used in a way that is ethical and beneficial to society.
3. Transformer Models and BERT Model: In this course, participants delve into the specifics of Transformer models and the Bidirectional Encoder Representations from Transformers (BERT) model. The content includes a detailed look at the components of Transformer architecture, such as the self-attention mechanism, and explores various applications, such as text classification and question answering. This course is ideal for those interested in the latest in natural language processing technologies.
4. Introduction to Large Language Models: This module explores Large Language Models (LLMs) and their applications. Learners will understand LLMs, their use cases, and how prompt tuning can enhance their performance. The course also includes information on using Google tools to develop LLM applications, providing practical insights into deploying these models.
5. Encoder-Decoder Architecture: The Encoder-Decoder architecture is fundamental for understanding how sequence-to-sequence tasks like text summarization and machine translation are approached in AI. This course explains the main components of this architecture and includes a practical lab where learners can code a simple Encoder-Decoder model using TensorFlow. This hands-on experience is invaluable for applying AI to linguistic tasks.
6. Attention Mechanism: This course introduces the attention mechanism, a critical component that enhances the performance of neural networks by allowing them to focus on specific parts of an input sequence. The module covers how attention is used in machine-learning tasks, including machine translation and text summarization. Learners will better understand how to improve model performance with attention techniques.
Each course is designed to take about 45 minutes to complete and offers a digital badge upon completion, aiding learners in showcasing their new skills on professional platforms. These courses provide a perfect foundation in AI, from understanding basic concepts to exploring advanced algorithms and architectures.
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