12,053 courses
This is a self-paced lab that takes place in the Google Cloud console. Use an inventory manager AppSheet app, configure connections via webhooks, and automate what will happen for some common changes in the data.
This is a self-paced lab that takes place in the Google Cloud console. Create a delivery pipeline using Google Cloud Deploy, create a release for a basic application, and promote the application through a series of Google Kubernetes Engine targets
這門課是「CAD/BIM技術與應用」專項課程的第六門課，屬於BIM系列課程：「工程資訊管理BIM基礎」、「工程資訊管理BIM塑模」及「工程資訊管理BIM應用」的第二門課。 修習這門課之前，應具備BIM技術的基礎知識，並能操作簡單的BIM模型，如此才能在修習這門課時，充分理解教授的內容與全觀BIM知識間的關聯。「CAD/BIM技術與應用」專項課程的「工程資訊管理BIM基礎」課程，可提供修習這門課前所需具備的相關知識。 BIM模型，是BIM技術應用的基礎。在這門課中，我們以業界廣為應用的建模軟體Autodesk Revit，作為教學工具。學習者將親自建置一棟六層樓住宅的BIM模型，並理解如何將工程施作流程，對應至BIM模型中。唯有依照工程施作流程建置BIM模型，BIM模型才得以實際應用在施工中。完成此課程後，將具備業界所需的BIM塑模能力。 想理解BIM模型是如何製作的嗎？想成為優秀的BIM模型建構師嗎？學習完這門課，你將了解工程實務與建置BIM模型間的對應關聯，並具備BIM塑模的技能。 預估所需學習總時間：45小時。
Introduction video: https://youtu.be/TRhwIHvehR0 This course is for a complete novice of Python coding, so no prior knowledge or experience in software coding is required. This course selects, introduces, and explains Python syntaxes, functions, and libraries that were frequently used in AI coding. In addition, this course introduces vital syntaxes, and functions often used in AI coding and explains the complementary relationship among NumPy, Pandas, and TensorFlow, so this course is helpful for even seasoned python users. This course starts with building an AI coding environment without failures on learners’ desktop or notebook computers to enable them to start AI modeling and coding with Scikit-learn, TensorFlow, and Keras upon completing this course. Because learners have an AI coding environment on their computers after taking this course, they can start AI coding and do not need to join or use the cloud-based services.
Zero-Shot & Few-Shot Learning is an intermediate-level course designed for data scientists, ML engineers, and AI practitioners who want to build models that perform well—even when labeled data is limited. Traditional supervised learning breaks down when examples are scarce or tasks are constantly evolving. This course shows you how to solve that problem using cutting-edge zero-shot and few-shot learning techniques. You'll learn how to apply pre trained models, semantic embeddings, and transfer learning to generalize across tasks without retraining from scratch. Through case-driven videos, hands-on labs, and decision-focused projects, you'll explore tools like prompt engineering, prototypical networks, and contrastive learning. Along the way, you'll build and defend full pipelines tailored to real-world constraints—choosing the right method based on data availability, task requirements, and deployment goals. Whether you're diagnosing fraud with few samples or classifying new product types without labels, this course will equip you to build smarter, leaner models that learn more with less.
In this hands-on course, you’ll learn how to move computer vision models from notebooks to the real world. You’ll build an end-to-end inference pipeline, package it into a reproducible API, and evaluate its performance using precision, recall, and mean Average Precision (mAP). You’ll also practice diagnosing errors, segmenting results by condition, and communicating insights like a professional MLOps engineer. By the end, you’ll be ready to deploy, evaluate, and iteratively improve vision models that teams can trust.
Integrate, Scale, and Monitor ML Microservices” is a hands-on course designed for learners who want to build reliable and scalable machine-learning services. You’ll explore how ML models fit into modern microservice architectures, learning to design clear service boundaries, integrate prediction services effectively, and choose communication patterns that improve resilience. The course also guides you through asynchronous workflows and scaling strategies used in real production systems. Finally, you’ll develop practical troubleshooting skills by interpreting logs, metrics, and distributed traces to diagnose performance issues. Through demos, reflective coach activities, and a realistic analysis project, you’ll gain the technical judgment and practical intuition needed to operate ML microservices with confidence.
This course introduces participants to ESG materiality and provides the tools needed to prioritize impactful initiatives. Learners will explore the concept of materiality, understand its significance in ESG practices, and gain practical skills to conduct Materiality Assessment Matrices. With a focus on asking the right questions and using essential tools, this course equips professionals to align ESG initiatives with organizational goals and drive meaningful outcomes.
This course will provide an in-depth exploration of desktop 3D printing hardware. It will examine the history of desktop 3D printing and demonstrate how 3D printers are made and how they work. This course will also provide step-by-step instructions for how to use and repair a 3D printer. It will also explore the different types of materials that can be 3D printed and will demonstrate how you can create various objects using these tools and materials. Learners who complete this course will be able to successfully operate, repair, and upgrade a 3D printer. In addition, learners who enroll in the course certificate will be able to purchase a desktop 3D printer at a discounted price (provided by Ultimaker). If you enjoy this business course and are interested in an MBA, consider applying to the iMBA, a flexible, fully-accredited online MBA at an incredibly competitive price offered by the University of Illinois. For more information, please see the Resource page in this course and onlinemba.illinois.edu.
The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.
This course offers students an opportunity to learn fundamentals of computation required to understand and analyze real world data. The course helps students to work with modern data structures, apply data cleaning and data wrangling operations. The course covers conceptual and practical applications of probability and distribution, cluster analysis, text analysis and time series analysis.
Al final de este proyecto, usted habrá aprendido a comercializar y anunciar su nuevo negocio usando Canva. Usted creará un logotipo de la empresa, una tarjeta de presentación y un folleto usando Canva. Crearemos diseños desde cero y usaremos plantillas que modificaremos. Usted podrá incorporar un esquema de color , imágenes, y otros elementos de diseño estético. En este proyecto usaremos la versión gratis de Canva. Nota: Este curso es de mayor utilidad para estudiantes que residen en la región de América del Norte. Actualmente, estamos trabajando para proporcionar la misma experiencia en otras regiones.
