Six free artificial intelligence (AI) courses offered by Google provide a beginner’s guide to exploring the realm of AI. These courses are designed to deliver fundamental concepts and practical applications in a comprehensive and manageable format, each estimated to take approximately 45 minutes for completion. On successful completion of each course, learners are rewarded with a digital badge to display their newly acquired skills on professional platforms.
The first course, “Introduction to Generative AI,” provides a basic overview of Generative AI. This course differentiates Generative AI from traditional machine learning techniques and explicates its practical applications. Learners can explore Google’s tools for creating AI-driven applications. Ideal for those curious about content generation and innovation through AI.
The “Introduction to Responsible AI” course delves into the ethical dimensions of AI technology. It discusses the importance of responsible AI in the development of AI systems. This course also hosts a thorough understanding of Google’s seven AI principles, guiding learners to implement AI responsibly.
The course on “Transformer Models and BERT Model” gets into the specifics of Transformer models and Bidirectional Encoder Representations from Transformers (BERT) model. It offers a detailed view of the components of Transformer architecture like the self-attention mechanism and its various applications. Those interested in natural language processing technologies would find this course helpful.
The fourth course is on “Introduction to Large Language Models (LLMs),” where learners can grasp the concept of LLMs, their applications, and how to enhance their performance through prompt tuning. The course also includes insights on using Google tools to develop LLM applications.
The “Encoder-Decoder Architecture” course imparts fundamental knowledge about how sequence-to-sequence tasks like text summarization and machine translation are implemented in AI. The course contains a practical lab for learners to code a simple Encoder-Decoder model using TensorFlow, boosting their hands-on experience in applying AI to linguistic tasks.
The last course, “Attention Mechanism,” introduces learners to the concept of an attention mechanism, a critical component that improves neural network performance by focusing on specific parts of an input sequence. The course covers how attention is used in machine-learning tasks, including machine translation and text summarization.
These free AI courses provide a sound foundation in AI, helping learners navigate from understanding basic concepts to exploring advanced algorithms and architectures.
