The following six free AI courses provide a structured pathway for beginners to begin 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 introduction to Generative AI, explaining what it is and how it differs from traditional machine learning techniques. Attendees will learn about applications of Generative AI and explore tools developed by Google to create their own AI-powered applications. This microlearning module is perfect for anyone interested in how AI can generate content and innovate across a variety of fields.
2. Introduction to Responsible AI: This course focuses on the ethical aspects of AI technologies. Introduces learners to responsible AI and explains why it is important in the development of AI systems. This course also describes his 7 principles of Google's AI and guides participants on how to responsibly implement AI in their projects. This is critical to ensuring that AI technology is used in an ethical and socially beneficial way.
3. Transformer and BERT models: In this course, participants delve into the details of transformer models and bidirectional encoder representation with transformers (BERT) models. This content includes details on the components of his Transformer architecture, such as self-attention mechanisms, and also discusses various applications such as text classification and question answering. This course is perfect for anyone interested in the latest natural language processing technologies.
4. Overview of Large-Scale Language Models: This module introduces Large-Scale Language Models (LLMs) and their applications. Learners will understand LLM, its use cases, and how quick tuning can improve performance. The course also includes information on developing his LLM applications using Google tools and provides practical insights on deploying these models.
5. Encoder-decoder architecture: The encoder-decoder architecture is fundamental to understanding how sequence-to-sequence tasks such as text summarization and machine translation are approached with AI. This course describes the main components of this architecture and includes a hands-on lab where learners can code a simple encoder/decoder model using his TensorFlow. This hands-on experience is invaluable in applying AI to language tasks.
6. Attention Mechanisms: This course introduces attention mechanisms, a key component that improves the performance of neural networks by allowing them to focus on specific parts of the input sequence. This module describes how attention is used in machine learning tasks such as machine translation and text summarization. Learners will gain a deeper understanding of how attention techniques can be used to improve model performance.
Each course is designed to take approximately 45 minutes to complete and upon completion provides a digital badge to help learners showcase their new skills on a professional platform. These courses provide the perfect foundation in AI, from understanding basic concepts to exploring advanced algorithms and architectures.
Niharika is a Technical Consulting Intern at Marktechpost. She is a third-year undergraduate and currently pursuing her bachelor's degree from the Indian Institute of Technology (IIT), Kharagpur. She is a very passionate person with a strong interest in machine learning, data science, and AI, and is avidly reading the latest trends in these fields.
