Google AI Essentials is a self-paced online course offered on Coursera, designed for professionals across industries to master foundational AI skills in under 10 hours. Developed by Google AI experts, the program requires no prior experience and focuses on practical applications of generative AI tools.

The ​"Prompt Design in Vertex AI" course is a hands-on, skill-focused program designed to equip learners with expertise in ​generative AI implementation and ​prompt engineering using Google Cloud's Vertex AI platform. Over 3 hours and 45 minutes, participants explore:

• ​​Prompt Engineering Fundamentals: Crafting effective prompts to control AI outputs, including zero-shot, one-shot, and few-shot techniques
​
• ​​Multimodal AI with Gemini: Leveraging Gemini models for text, image, and mixed-data scenarios, such as analyzing marketing visuals and generating contextual content
• ​​Real-World Applications: Applying skills to marketing use cases (e.g., ad copy generation, product imagery analysis) through guided labs.
• ​​Tool Proficiency:​ Hands-on practice with ​Vertex AI Studio and ​Gemini API for prototyping and deploying AI solutions.

Earning the ​Prompt Design in Vertex AI Skill Badge validates mastery of these competencies.

This course, offered through Google Cloud Skills Boost, is designed to help aspiring candidates prepare for the ​Professional Machine Learning Engineer (PMLE) certification exam. It provides a structured framework to explore the exam domains, assess knowledge gaps, and build a personalized study plan. The course emphasizes practical readiness by guiding learners through the scope of the exam, identifying skill deficiencies, and recommending targeted resources. Ideal for professionals seeking Google Cloud's PMLE certification, it combines strategic planning with actionable insights to optimize exam success.

This course, available via Google Cloud Skills Boost, provides a comprehensive introduction to Google Cloud's AI and machine learning (ML) technologies. Designed for data scientists, AI developers, and ML engineers, it explores the end-to-end data-to-AI lifecycle, covering foundational concepts, development tools, and generative AI solutions. Through hands-on labs and real-world examples, learners gain practical experience with Google Cloud's infrastructure (e.g., Vertex AI, BigQuery ML), pre-trained APIs, AutoML, and generative AI toolkits like Gemini. The course emphasizes building predictive and generative AI projects while navigating Google Cloud's ecosystem.

This course, available via Google Cloud Skills Boost, provides a comprehensive introduction to Google Cloud's AI and machine learning (ML) technologies. Designed for data scientists, AI developers, and ML engineers, it explores the end-to-end data-to-AI lifecycle, covering foundational concepts, development tools, and generative AI solutions. Through hands-on labs and real-world examples, learners gain practical experience with Google Cloud's infrastructure (e.g., Vertex AI, BigQuery ML), pre-trained APIs, AutoML, and generative AI toolkits like Gemini. The course emphasizes building predictive and generative AI projects while navigating Google Cloud's ecosystem.

This course, available on Google Cloud Skills Boost, provides a foundational understanding of ​Vertex AI Notebooks, a Jupyter-based environment designed to streamline the end-to-end machine learning workflow. Learners explore the features and use cases of two key notebook solutions: ​Vertex AI Colab Enterprise and ​Vertex AI Workbench Instances. Through guided labs and video tutorials, participants gain hands-on experience in creating, managing, and leveraging notebooks for tasks like exploratory data analysis (EDA) using BigQuery. Ideal for data scientists and ML engineers, the course emphasizes practical skills for unifying data preparation, model development, and deployment within Google Cloud's AI ecosystem.

The "Create ML Models with BigQuery ML" skill badge course on Google Cloud Skills Boost equips learners with hands-on experience in building, evaluating, and deploying machine learning models directly within Google BigQuery. Designed for data professionals, this intermediate-level program focuses on leveraging BigQuery ML—a tool that simplifies ML workflows using SQL—to solve real-world prediction tasks like customer purchase behavior, taxi fare forecasting, and sports bracketology. By completing five structured labs, participants gain practical expertise in classification, regression, and time-series modeling while working with large-scale public datasets.

The ​"Engineer Data for Predictive Modeling with BigQuery ML" skill badge course on Google Cloud Skills Boost focuses on end-to-end data engineering and predictive modeling workflows. Designed for intermediate learners, it teaches how to build scalable ETL (Extract, Transform, Load) pipelines using tools like ​Cloud Dataprep by Alteryx and ​Dataflow, while integrating BigQuery ML to create and deploy machine learning models. Participants learn to transform raw data into actionable insights, covering data preparation, pipeline automation, and logistic regression models for real-world scenarios like customer purchase prediction.

The "Feature Engineering" course on Google Cloud Skills Boost equips learners with advanced techniques to optimize machine learning models through effective data transformation. Designed for data professionals, this course focuses on leveraging ​Vertex AI Feature Store for centralized feature management and teaches practical skills to identify, create, and refine high-impact features using ​BigQuery ML, Keras, TensorFlow, and Dataflow. Participants will learn to bridge raw data to actionable insights, covering feature crosses, preprocessing with Apache Beam, and automated workflows with ​TensorFlow Transform (tf.Transform). Ideal for those aiming to enhance model accuracy and streamline ML pipelines.

The "Build, Train and Deploy ML Models with Keras on Google Cloud" course on Google Cloud Skills Boost is designed for data scientists and ML engineers seeking to master end-to-end machine learning workflows using industry-standard tools. Participants learn to construct robust TensorFlow input pipelines with the tf.data library, design neural networks via Keras APIs (Sequential and Functional), and deploy scalable models using ​Vertex AI. The curriculum emphasizes practical skills for improving model accuracy, handling large datasets, and productionalizing ML solutions. Ideal for intermediate learners, the course combines hands-on labs with theoretical insights, preparing users to tackle real-world ML challenges in cloud environments.