12 053 cours
Este curso ensina a criar modelos de ML com o TensorFlow e o Keras, melhorar a acurácia deles e desenvolver modelos para uso em escala.
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Kursus ini memperkenalkan topik penting tentang privasi dan keamanan AI. Kursus ini mengeksplorasi metode dan alat praktis untuk menerapkan rekomendasi praktik privasi dan keamanan AI melalui penggunaan produk dan alat open source Google Cloud.
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This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to leverage NetApp Cloud Volumes ONTAP for SMB workloads.
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This long course focuses on the operational lifecycle of agentic AI systems: robust partitioning and dataset management, automated retraining pipelines, continuous monitoring for drift and anomalies, testing and secure deployment, and performance optimization of code and pipelines. You will practice partitioning strategies (time-series and stratified), monitoring and drift detection metrics (PSI and KS), and build CI/CD notebooks and automated workflows for model retraining and re-deployment using tools like MLflow and GitHub Actions. The course addresses software-engineering best practices—clean code, profiling, unit and integration testing—and dependency risk assessment to maintain secure, reliable production systems. Practical assignments include building monitoring alerting rules, implementing retraining triggers, diagnosing runtime bottlenecks, and integrating human-in-the-loop feedback systems to continuously improve models in production while ensuring high code quality and security hygiene.
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Why do smart people sometimes think they are right when they are very, very wrong? Our brains are designed to make quick decisions, and sometimes we miss out on what’s really going on. Knowing this, Professor Sydney Finkelstein walks you through the neuroscience behind decision-making, and teaches you to make conscious decisions that are right for you at work and in life. By teaching you to reflect and move forward even better than before, Professor Finkelstein will help you to be the best you possibly can at whatever you do.
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このコースでは、Google Cloud の生成 AI を活用したコラボレーターである Gemini が、クラウド環境とリソースを安全に保つうえでどのように役立つかを学習します。サンプル ワークロードを Google Cloud の環境にデプロイする方法、Gemini を使用してセキュリティ構成ミスを特定、修正する方法を学びます。ハンズオンラボでは、Gemini を使用することでクラウドのセキュリティ ポスチャーがどのように向上するかを体験します。 Duet AI は、次世代モデルである Gemini に名称変更されました。
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Learning outcomes: Design a Windows/DirectX app lifecycle; configure devices, contexts, and swap chains; implement shaders and the graphics pipeline; render frames with clean event loops; handle input and window resizing; optimize presentation with flip-model swaps; and debug, refactor, and finalize a leak-free build. This beginner-friendly course guides learners from “nothing on screen” to a stable, interactive Direct3D 11 application. You’ll set up a runnable pipeline, bind render targets and viewports, and validate with a Hello Direct3D frame before advancing to drawing, texturing, and frame presentation. Along the way, you’ll integrate Win32 input, structure Update/Render/Present for responsiveness, and build a simple star-field demo to practice per-frame updates. Final modules focus on production readiness: resize-safe swap chain management, input/vertex layouts, class-based architecture, and disciplined COM resource cleanup. What makes this course unique is its end-to-end, code-first path that balances fundamentals with real-world details—like choosing formats, using flip-sequential presentation, and organizing rendering code for maintainability. By the end, learners confidently deploy a clear, well-structured DirectX app and know how to extend it.
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Every modern organization is a digital organization or will rapidly become digital. Artificial intelligence, Google/Amazon/Facebook/Uber, and big data have dramatically raised customer expectations and demand. Organizations that are effective in using data will win in the economies of the mid-21st century. These must-have core competencies include data analysis, machine learning, data visualizations, data mining, and predictive analytics, and deep learning. Organizations that won't or can't digitally transform will go the way of Blockbuster or Border's Bookstore. The organization that better harnesses the power of data to create a superior customer experience will thrive in the new business realities. The question is, how does an organization digitally transform? There are many digital technologies for organizations to choose from - too many choices! And digital technologies are only part of creating a digital organization. The employees must be trained in the new technologies, leaders must learn how to use data in making strategic decisions, and the organization's business processes must be reinvented. So many choices to make and the stakes have never been higher! This course will give you a framework to help you successfully navigate the challenges posed by digital transformation. First, we will discuss how to use the organization's dynamic capabilities to start the digital transformation. Second, we will use fitness landscapes to build a competitive digital business model. Finally, we will implement a strategic foresight function to help evolve the digital business model for the organization's continued success.
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このコースでは、AI の解釈可能性と透明性のコンセプトを紹介します。デベロッパーとエンジニアにとって AI の透明性が重要であることについて説明します。データと AI モデルの両方で解釈可能性と透明性を達成できる実践的な方法とツールを検証します。
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By completing this course, learners will gain the ability to configure shared resources, implement FTP file transfers, design JMS-to-database workflows, and test integration processes in TIBCO BusinessWorks. They will develop skills to create reusable connections, apply schema-driven data formats, update relational databases, and validate end-to-end integrations with robust error handling. This project-based course provides hands-on practice with real-world scenarios like file transfers between demand systems and HEB databases. Unlike generic tutorials, it focuses on a complete grocery store integration case study, helping learners apply TIBCO BW concepts in a practical, industry-relevant context. Participants will also explore structured testing techniques with Timer activities, ensuring their processes are reliable and production-ready. By the end of the course, learners will be able to design, implement, and test scalable integration solutions using TIBCO Designer. This course is ideal for integration developers, architects, and IT professionals who want to enhance their expertise in enterprise-level integration workflows.
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In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks. After a project predicting likelihood of heart disease given health characteristics, we’ll move to random forests. We’ll describe the differences between the two techniques and explore their differing origins in detail. Finally, we’ll complete a project predicting similarity between health patients using random forests.
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Este curso introductorio de programación en Python está dirigido a estudiantes universitarios de primeros semestres, estudiantes de secundaria y profesionales que deseen adquirir una base sólida en el desarrollo de software. A través de seis módulos progresivos, se enseñarán los fundamentos de la programación, desde los conceptos básicos hasta la resolución de problemas reales mediante algoritmos y estructuras de control. Al finalizar el curso, los participantes estarán en capacidad de comprender y aplicar los principios esenciales de la programación con Python. Se desarrollarán competencias clave como el diseño de algoritmos, el uso adecuado de estructuras de control (condicionales y ciclos), la creación de funciones reutilizables y la depuración de código. Además, podrán estructurar programas de forma lógica, utilizar librerías especializadas y aplicar herramientas fundamentales del lenguaje. Este curso no solo enseña a programar, sino a pensar con precisión y lógica. Gracias a su enfoque práctico y gradual, constituye una experiencia formativa integral que permite adquirir habilidades técnicas y resolver problemas computacionales de manera eficaz, preparando a los estudiantes para futuros retos académicos y profesionales en el ámbito tecnológico.
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