Google plays a crucial role in advancing AI by developing cutting-edge technologies and tools like TensorFlow, Vertex AI, and BERT. Its AI courses provide valuable knowledge and hands-on experience, helping learners build and optimize AI models, understand advanced AI concepts, and apply AI solutions to real-world problems. This article lists the top AI courses by Google that provide comprehensive training on various AI and machine learning technologies, equipping learners with the skills needed to excel in the rapidly evolving field of AI.
Introduction to AI and Machine Learning on Google Cloud
This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It helps data scientists, AI developers, and ML engineers enhance their skills through engaging learning experiences and practical exercises.
Feature Engineering
This course explores the benefits of Vertex AI Feature Store, improving ML model accuracy and identifying useful data features. It includes labs on feature engineering with BigQuery ML, Keras, and TensorFlow.
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
This course covers using multimodal prompts to extract information from text and visual data and generate video descriptions with Gemini. Participants learn to build metadata for documents containing text and images, retrieve relevant text chunks, and print citations using Multimodal RAG with Gemini.
TensorFlow on Google Cloud
This course covers designing TensorFlow input data pipelines and building ML models with TensorFlow and Keras. Participants learn how to improve model accuracy and write scalable, specialized ML models.
Computer Vision Fundamentals with Google Cloud
This course covers computer vision use cases and machine learning strategies, from using pre-built ML APIs to building custom image classifiers with linear, DNN, or CNN models. It teaches model accuracy improvement techniques and practical solutions for data limitations. Learners will gain hands-on experience with image classification models using public datasets.
Natural Language Processing on Google Cloud
This course introduces Google Cloud products and solutions for solving NLP problems. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow.
Production Machine Learning Systems
This course covers implementing static, dynamic, and continuous production ML systems, as well as batch and online processing. Participants learn about TensorFlow abstraction levels, distributed training options, and writing custom estimator models.
Introduction to Generative AI
This introductory microlearning course explains Generative AI, its applications, and its differences from traditional machine learning. It also includes guidance on using Google Tools to develop your own Generative AI applications.
Introduction to Large Language Models
This course covers large language models (LLMs), their use cases, and how to enhance their performance with prompt tuning. Just like the previous course, this one also includes guidance on using Google tools to develop your own Generative AI apps.
Transformer Models and BERT Model
This course introduces the Transformer architecture and the BERT model, covering components like the self-attention mechanism. It covers how BERT is built and used for tasks such as text classification, question answering, and natural language inference.
Introduction to Responsible AI
This course explains what responsible AI is, its importance, and how Google implements it in its products. It also introduces Google’s 7 AI principles.
Introduction to Vertex AI Studio
This course introduces Vertex AI Studio for prototyping and customizing generative AI models. It teaches about the generative AI workflow and how to use Vertex AI Studio for Gemini multimodal applications, prompt design, and model tuning.
Prompt Design in Vertex AI
This course covers prompt engineering, image analysis, and multimodal generative techniques in Vertex AI. Participants learn to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
Responsible AI: Applying AI Principles with Google Cloud
This course teaches how to operationalize responsible AI in your organization using Google Cloud’s best practices and lessons learned. It also teaches the importance of building AI responsibly as enterprise use grows and covers the practical frameworks for developing your own responsible AI approach.
Vector Search and Embeddings
This course introduces Vertex AI Vector Search and how to build a search application using LLM APIs for embeddings. It includes lessons on vector search and text embeddings, practical demos, and a hands-on lab.
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