Top AI Courses Offered by Google

Google has long played a critical role in advancing artificial intelligence by developing cutting-edge technologies such as TensorFlow, Vertex AI, and BERT; Google’s AI courses offer valuable insights and practical experience in building and optimizing AI models, understanding advanced concepts, and applying AI solutions to real-world problems; This article presents top AI courses offered by Google, designed to provide comprehensive training and develop the essential skills needed to excel in the rapidly evolving field of AI.

Introduction to AI and Machine Learning

Introduction to AI and Machine Learning on Google Cloud is a foundational course that introduces Google Cloud’s AI and ML offerings for predictive and generative projects; It covers a range of technologies, products, and tools spanning the entire data-to-AI lifecycle; This course aims to upskill data scientists, AI developers, and ML engineers with engaging learning experiences and practical exercises.

Feature Engineering focuses on the benefits of Vertex AI Feature Store for improving ML model accuracy and identifying useful data features; Learners will experience hands-on labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

Inspect Rich Documents with Gemini Multimodality and Multimodal RAG delves into extracting information from text and visual data, generating video descriptions with Gemini; The course also teaches building metadata, retrieving relevant text chunks, and printing citations using Multimodal RAG with Gemini.

Deep Dive into TensorFlow and Computer Vision

TensorFlow on Google Cloud offers an in-depth exploration of designing TensorFlow input data pipelines; Participants will build ML models using TensorFlow and Keras, learn how to improve model accuracy, and write scalable, specialized ML models.

Computer Vision Fundamentals with Google Cloud addresses computer vision use cases and strategies; From using pre-built ML APIs to building custom image classifiers with models like linear, DNN, or CNN, it covers techniques for improving model accuracy and practical solutions for data limitations; Learners gain hands-on experience with image classification models using public datasets.

Natural Language Processing on Google Cloud introduces Google Cloud products and solutions for tackling NLP problems; It encompasses developing NLP projects using neural networks with Vertex AI and TensorFlow.

Production-Ready ML Systems

Production Machine Learning Systems focuses on implementing various production ML systems like static, dynamic, and continuous, along with batch and online processing; The course teaches TensorFlow abstraction levels, distributed training options, and writing custom estimator models.

Introduction to Generative AI is an introductory microlearning course explaining what Generative AI is, its applications, and differences from traditional machine learning; The course also guides learners on using Google Tools to develop their Generative AI applications.

Introduction to Large Language Models delves into large language models (LLMs), their use cases, and performance enhancement techniques with prompt tuning; Like the previous course, it also includes guidance on using Google tools to develop Generative AI applications.

Advanced NLP and Responsible AI Practices

Transformer Models and BERT Model offers an introduction to the Transformer architecture and the BERT model; It covers components such as the self-attention mechanism and applications for text classification, question answering, and natural language inference.

Introduction to Responsible AI explains the principles of responsible AI, its importance, and Google’s implementation strategies for its products; This course also introduces Google’s seven AI principles.

Introduction to Vertex AI Studio is aimed at introducing Vertex AI Studio for prototyping and customizing generative AI models; Learners will get to know the generative AI workflow and how to use Vertex AI Studio for Gemini multimodal applications, including prompt design and model tuning.

Optimizing Prompt Design and Vector Search

Prompt Design in Vertex AI covers the essentials of prompt engineering, image analysis, and multimodal generative techniques in Vertex AI; Participants will learn to craft effective prompts, guide generative AI output, and apply Gemini models to marketing scenarios.

Responsible AI: Applying AI Principles with Google Cloud helps in operationalizing responsible AI in organizations using Google Cloud’s best practices and lessons learned; It covers the significance of building AI responsibly and practical frameworks for developing a responsible AI approach.

Vector Search and Embeddings introduces Vertex AI Vector Search and includes the creation of a search application using LLM APIs for embeddings; Lessons cover vector search, text embeddings, practical demos, and hands-on labs.

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