12,053 courses
클라우드 컴퓨팅 기초(Cloud 101)에 오신 것을 환영합니다. 앞으로 몇 주에 걸쳐 클라우드 컴퓨팅의 기초에 대해 논의하게 됩니다. 여기에서는 클라우드 컴퓨팅이 무엇인지, 무엇을 지원하며, 어떻게 제공되는지를 알아봅니다. 스토리지 서비스, 클라우드 경제성, 관리형 인프라 수준 및 Azure 서비스에 대해 알아봅니다. 또한 클라우드 컴퓨팅의 다양한 배포 모델과 여러 호스팅 시나리오를 살펴봅니다. 마지막으로, 몇 가지 클라우드 플랫폼을 비교하고 클라우드 컴퓨팅의 미래에 대해 논의할 것입니다.
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build several Data Pipelines that will ingest data from a publicly available dataset into BigQuery.
By the end of this project, you will create a sample social media graphic for business. The graphic is shareable across your business’ social media platforms. You will be able to incorporate the color palette from your business style guide, in order to create brand recognition as part of your social media marketing strategy. This course will include an introduction to Canva, how to determine your canvas size for a social media post, incorporate images specific to your business into a social media post, and prepare a post for your social media channels. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
This course is an introduction to the study of bodies in motion as applied to engineering systems and structures. We will study the dynamics of particle motion and bodies in rigid planar (2D) motion. This will consist of both the kinematics and kinetics of motion. Kinematics deals with the geometrical aspects of motion describing position, velocity, and acceleration, all as a function of time. Kinetics is the study of forces acting on these bodies and how it affects their motion. --------------------------- Recommended Background: To be successful in the course you will need to have mastered basic engineering mechanics concepts and to have successfully completed my courses en titled an “Introduction to Engineering Mechanics” and “Applications in Engineering Mechanics.” We will apply many of the engineering fundamentals learned in those classes and you will need those skills before attempting this course. --------------------------- Suggested Readings: While no specific textbook is required, this course is designed to be compatible with any standard engineering dynamics textbook. You will find a book like this useful as a reference and for completing additional practice problems to enhance your learning of the material. --------------------------- The copyright of all content and materials in this course are owned by either the Georgia Tech Research Corporation or Dr. Wayne Whiteman. By participating in the course or using the content or materials, whether in whole or in part, you agree that you may download and use any content and/or material in this course for your own personal, non-commercial use only in a manner consistent with a student of any academic course. Any other use of the content and materials, including use by other academic universities or entities, is prohibited without express written permission of the Georgia Tech Research Corporation. Interested parties may contact Dr. Wayne Whiteman directly for information regarding the procedure to obtain a non-exclusive license.
Master market risk measurement, VaR, and financial models used by top risk professionals. Learn how to apply real-world risk frameworks and excel in FRM II. This course provides a comprehensive, practical approach to market risk measurement and management, covering key concepts such as Value at Risk (VaR), Expected Shortfall, correlation modeling, and hedging strategies. You will explore advanced topics including interest rate models, fixed income valuation, volatility modeling, and option pricing using Black-Scholes and binomial trees. Designed for finance professionals and FRM candidates, this course bridges the gap between theory and real-world application. You will learn how to analyze portfolio risk, implement hedging techniques, and evaluate financial models used in modern risk management. By the end of this course, you will be able to confidently apply market risk frameworks, interpret model outputs, and understand regulatory requirements such as the Internal Model Approach (IMA). Whether you're preparing for the FRM exam or advancing your career in finance, this course equips you with industry-relevant skills.
這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we will begin with an exploration of the field and profession of data science with a focus on the skills and ethical considerations required when working with data. We will review the types of business problems data science can solve and discuss the application of the CRISP-DM process to data mining efforts. A brief overview of Descriptive, Predictive, and Prescriptive Analytics will be provided, and we will conclude the course with an exploratory activity to learn more about the tools and resources you might find in a data science toolkit.
This course teaches you to build, track, and deploy machine learning models on the Databricks platform using MLflow. You start with the reproducibility crisis in ML — understanding why untracked experiments, scattered notebooks, and missing version control create production failures — and learn how MLflow solves these problems with structured experiment tracking, model versioning, and artifact management. You then explore MLflow's architecture in depth: the Tracking layer for logging parameters, metrics, and artifacts; the Model Registry for governance and stage gates; and the Projects layer for reproducible environments. The course covers Feature Store architecture for eliminating training/serving skew, where features are computed once and served two ways — batch for training and real-time for inference. You progress through the ML algorithm spectrum from manual implementations to AutoML, learning when to choose transparency over automation for regulated industries. The second module focuses on production deployment: the MLOps maturity staircase (L0 through L3), inference patterns for batch and real-time serving, and the infrastructure decisions that separate prototype ML from production ML. Hands-on labs on Databricks reinforce every concept.
The determinants of drug utilization are complex and multifactorial, and understanding how, why, when and where drugs are used is crucial to inform regulatory and payment policy and clinical practice. We begin by reviewing methods of investigating drug utilization and evaluating interventions to prescribing. We then explore common challenges to optimizing medicine use, including non-adherence and off-label drug use. We also consider methods to improve the quality of drug utilization, including value-based insurance designs, audit and feedback, patient education and medication therapy management. We also consider varied patient, provider, practice and system-level determinants of prescription drug utilization, including marketing and promotion, emerging evidence of benefits and harms, regulation and changes in coverage and reimbursement. Finally, we use four case studies to reinforce many of the core topics we’ve explored: antibiotics, prescription opioids, anticoagulants and generics and biosimilars.
À la fin de ce projet, vous serez capable de créer un exemple de graphique de réseaux sociaux pour les entreprises. Le graphique est partageable sur les plateformes de réseaux sociaux de votre entreprise. Vous serez en mesure d’intégrer la palette de couleurs de votre guide de style d’affaires, afin de créer la reconnaissance de la marque dans le cadre de votre stratégie de marketing sur les réseaux sociaux. Ce cours comprendra une introduction à Canva, comment déterminer la taille de votre toile pour un message sur les réseaux sociaux, intégrer des images spécifiques à votre entreprise dans un message sur les réseaux sociaux, et préparer un message pour vos canaux sociaux. Remarque : ce cours fonctionne le mieux pour les étudiants basés en Amérique du Nord. Nous nous efforçons actuellement d'apporter la même expérience dans d'autres régions.
This course teaches you how to build and manage data warehouses and pipelines using Microsoft Fabric. You will get hands-on experience by creating data warehouses, designing schemas, and developing ETL/ELT pipelines with Data Factory. The course covers the use of T-SQL for data transformations and incorporates version control for data workflows, ensuring you can manage production environments confidently. By the end of this course, you will be able to orchestrate data pipelines, implement security, and handle data-to-insights workflows from ingestion through security and deployment.
In diesem Kurs erfahren Sie, wie Gemini, ein auf generativer KI basierendes Produkt von Google Cloud, Network Engineers beim Erstellen, Aktualisieren und Warten von VPC-Netzwerken unterstützt. Sie lernen die Prompts kennen, mit denen Gemini spezifische Hilfestellungen für Ihre netzwerkbezogenen Aufgaben geben kann – und entdecken Möglichkeiten, die über eine Suchmaschine hinausgehen. In einem praxisorientierten Lab können Sie sich davon überzeugen, wie Gemini die Arbeit mit Google Cloud VPC-Netzwerken vereinfacht. Duet AI wurde umbenannt in Gemini, unser Modell der nächsten Generation.
