12 053 cours
This is a self-paced lab that takes place in the Google Cloud console. This lab teaches you how to ingest data from a CSV file into tables in BigQuery.
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This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to: Choose the right LLM architecture and model for your application using services. Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration Monitor and log metrics from your LLM to detect issues and continuously improve quality Build reliable and secure pipelines to train, deploy and update models using AWS services Comply with regulations when deploying LLMs in production through techniques like differential privacy and controlled rollouts This course is unique in its focus on real-world operationalization of large language models using AWS. You will work through hands-on labs to put concepts into practice as you learn. Whether you are a machine learning engineer, data scientist or technical leader, you will gain practical skills to run LLMs in production.
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이 과정은 데이터 준비부터 모델 배포와 모니터링까지 전체 머신러닝 워크플로를 위한 통합 플랫폼을 제공하는 Jupyter 노트북 기반 환경인 Vertex AI Notebooks를 소개합니다. 이 과정에서는 (1) 다양한 유형의 Vertex AI Notebooks와 그 특징, (2) Vertex AI Notebooks를 만들고 관리하는 방법을 다룹니다.
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By the end of this course, learners will be able to design, build, and deploy interactive R Shiny web applications to analyze and visualize data for real-world NGO use cases. Learners will gain hands-on experience creating responsive dashboards, implementing reactive programming, customizing visualizations, and enabling user-driven data exploration through advanced input controls. This course benefits learners by bridging the gap between data analysis and web application development using R, without requiring prior web development knowledge. Participants will learn how to transform static analyses into dynamic, user-friendly applications that empower non-technical stakeholders to interact with data, uncover insights, and support evidence-based decision-making. What makes this course unique is its project-based, NGO-focused approach, guiding learners step by step through the creation of a complete Shiny application—from foundational concepts and best practices to performance optimization and cloud deployment. Through a comprehensive case study, learners will build a flexible, production-ready data analysis web app while mastering industry-relevant Shiny skills that are directly applicable to social impact, analytics, and data-driven roles.
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블록체인 전문화의 첫 번째 과정은 블록체인 기술의 필수 개념에 대한 광범위한 개요를 제공합니다. 이는 비트코인 프로토콜에 이어 이더리움 프로토콜을 탐색함으로써 애플리케이션 및 프로그래밍 개발에 필요한 기반을 마련해 줍니다. 개인 이더리움 블록체인에서 노드 생성, 계정 생성, 계정 잠금 해제, 채굴, 거래, 이더 전송, 잔액 조회 등에 필요한 지식을 갖추게 됩니다. 분산형 피어 투 피어 네트워크, 불변의 분산 원장, 블록체인을 정의하는 신뢰 모델에 대해 배울 것입니다. 이 과정에서는 블록체인의 기본 구성 요소(트랜잭션, 블록, 블록 헤더 및 체인)와 기본 알고리즘의 작동(검증, 검증, 합의 모델) 및 신뢰의 필수 요소(하드포크 및 소프트포크)에 대해 설명할 수 있습니다. 콘텐츠에는 블록체인 프로그래밍에 필수적인 해싱 및 암호화 기반이 포함되며, 이는 이후 두 가지 전문화 과정인 스마트 컨트랙트와 분산 애플리케이션(Dapps)의 초점이 됩니다. 당신은 이더리움 테스트 체인을 구축하고 체인에서 작동하기 위해 이 과정을 위해 특별히 만들어진 가상 머신 이미지를 작업할 것입니다. 이 실습 활동은 블록체인의 작동, 거래, 블록 및 마이닝을 이해하는 데 도움을 줍니다. 주요 개념은 비디오, 데모 및 실습을 통해 전달됩니다.
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En este segundo curso se provee al participante de los fundamentos básicos del análisis de sitios y páginas web en sus diferentes modalidades, incluyendo los de comercio digital. Además, brinda al estudiante las herramientas para evaluar la efectividad de las campañas utilizadas para generar tráfico a estos sitios a través de los anuncios en buscadores y sitios en general. Incluye también la medición de la eficiencia de las campañas de correo electrónico. Parte fundamental del curso es el desarrollo de habilidades analíticas aplicadas en el contexto de mercadotecnia. Los objetivos del curso 2 son los siguientes: • Desarrollar en el participante el pensamiento analítico aplicado a la mercadotecnia digital. • Aprender a utilizar las plataformas analíticas disponibles en el mercado para estudiar sitios web, tiendas online y campañas digitales. • Generar estrategias a partir del análisis de datos generados por la mercadotecnia digital.
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In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets. This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
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Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars. Similar problems also underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional undergraduate computer science methods. This course is intended for students who have completed Basic Modelling for Discrete Optimization. In this course you will learn much more about solving challenging discrete optimization problems by stating the problem in a state-of-the-art high level modeling language, and letting library constraint solving software do the rest. This course will focus on debugging and improving models, encapsulating parts of models in predicates, and tackling advanced scheduling and packing problems. As you master this advanced technology, you will be able to tackle problems that were inconceivable to solve previously. Watch the course promotional video here: https://www.youtube.com/watch?v=hc3cBvtrem0&t=8s
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In this course, you will learn about Agile software development, offering a practical understanding of the software development life cycle (SDLC) with a strong emphasis on Agile methodologies.
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Welcome to "Simulation of Covid-19 Testing Process Using R Simmer". This is a project-based course which should take under 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, learn gain introductiory knowledge of Discrete Event Simulation, use R Studio and Simmer library, create statistical variables required for simulation, define process trajectory, define and assign resources, define arrivals (eg. incoming customers / work units), run simulation in R, store results in data frames, plot charts and interpret the results.
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Learn how theatre and globalization have affected each other over the past century, and how to conduct your own research on global theatre histories.
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In this 1-hour long project-based course, you will learn how to Access an EC2 instance shell from the AWS console Amazon Elastic Compute Cloud is the service you use to create and run virtual machines (VM), also known as instances. By completing the steps in this guided project, you will successfully launch a Linux VM on Amazon EC2 within the AWS Free Tier. You will also connect to the instance that you launch using the AWS management console and then deploy a web application using the AWS management console.
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