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• Technologie

  Intermédiaire

  Bayesian Regression and Model Selection

  Master Bayesian modeling through Bayesian linear regression, generalized linear models, hierarchical models and model selection. This course will deepen your understanding of modeling techniques and the importance of the prior when contrasted with traditional frequentist modeling approaches. You will understand the benefits of hierarchical models and how they automatically identify the right amount of pooling between data to provide a balance between the complete and no pooling approaches. You will learn how to apply posterior predictive checks for model selection and understand the Occam’s razor principle. This course combines theoretical modeling foundations with hands-on implementations.

  Langue d’origine

  Voir les détails: Bayesian Regression and Model Selection
• Affaires

  Débutant

  Liderazgo y Desarrollo Personal

  Todos hemos tenido alguna experiencia en relación con el liderazgo, ya sea propia o a través de la observación de otros, pero difícilmente podamos definirla. Si nos preguntamos ¿qué es el liderazgo?, existirán tantas definiciones como personas a las que interpelemos. Lo que veremos en el curso tratará de tener la mejor perspectiva del liderazgo actual, definiéndolo no solo en torno a una relación gestor - colaborador dentro de una organización, sino a toda situación en la cual alguien intenta influenciar los comportamientos de otras personas o grupos. Objetivos generales: • Conocer el ADN y los elementos más relevantes del liderazgo. • Analizar la importancia de la diversidad e inclusión en los grupos de trabajo. • Incorporar herramientas de creatividad e innovación como clave para el management. • Examinar las nuevas formas de trabajo en las compañías del presente y para el futuro.

  Langue d’origine

  Voir les détails: Liderazgo y Desarrollo Personal
• Affaires

  Tous les niveaux

  Analyze & Evaluate Banking Risk Management Frameworks

  Develop the ability to analyze, evaluate, and manage key banking risks using structured risk management frameworks. Learners will assess credit risk governance, design liquidity and interest rate risk controls, evaluate foreign exchange and price risk exposures, and apply operational and compliance risk management tools aligned with regulatory expectations. This course provides a comprehensive, practice-oriented understanding of risk management in banks, covering governance structures, board oversight, risk appetite, internal controls, MIS reporting, and portfolio-level risk monitoring. Unlike fragmented risk courses, this program integrates all major banking risk categories—credit, liquidity, interest rate, foreign exchange, price, operational, and compliance risk—into one cohesive framework. By completing this course, learners will strengthen their ability to interpret risk reports, evaluate exposure limits, apply measurement techniques such as gap and duration analysis, and understand how ALCO, internal audit, and senior management collaborate in maintaining financial stability. The course is ideal for banking professionals, risk analysts, auditors, finance students, and regulators seeking structured, real-world risk management expertise.

  Langue d’origine

  Voir les détails: Analyze & Evaluate Banking Risk Management Frameworks
• Affaires

  Tous les niveaux

  DeFi: Crypto Staking Masterclass: Passive Income with Web3

  Start earning passive income with DeFi - even as a beginner. In this practical course, you'll learn how to stake cryptocurrency using real DeFi platforms, understand how staking works behind the scenes, and build a foundational strategy for earning income in the world of Web3 and blockchain finance. Staking is one of the most powerful and accessible ways to earn with crypto - but it's also filled with risks, scams, and confusing tools. This course walks you through the entire staking process step by step, from basic concepts to advanced techniques, while using real platforms and case studies. What You’ll Learn: What DeFi is and how it differs from centralized crypto services How cryptocurrency staking works (proof of stake, APY, validators, etc.) Where to stake your coins (DeFi platforms vs exchanges) How to analyze staking rewards and risks How to use platforms like MetaMask, Ledger, and DeFi dashboards Smart contract safety tips and red flags to avoid The difference between staking, yield farming, and liquidity mining How to earn with real assets like ETH, SOL, ADA, and stablecoins Strategies for sustainable passive income with crypto This Course Is Perfect For: Crypto beginners who want to earn passive income Web3 enthusiasts curious about staking and DeFi Investors looking to diversify with blockchain assets Anyone confused by staking jargon and wanting a hands-on walkthrough With real examples, walkthroughs, and strategies, this course will give you the confidence to navigate decentralized finance and start earning from your crypto. Enroll now to take your first step into DeFi and crypto staking.

  Langue d’origine

  Voir les détails: DeFi: Crypto Staking Masterclass: Passive Income with Web3
• Technologie

  Avancé

  AI Workflow: AI in Production

  This is the sixth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.     This course focuses on models in production at a hypothetical streaming media company.  There is an introduction to IBM Watson Machine Learning.  You will build your own API in a Docker container and learn how to manage containers with Kubernetes.  The course also introduces  several other tools in the IBM ecosystem designed to help deploy or maintain models in production.  The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow.   By the end of this course you will be able to: 1.  Use Docker to deploy a flask application 2.  Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3.  Discuss basic Kubernetes terminology 4.  Deploy a scalable web application on Kubernetes  5.  Discuss the different feedback loops in AI workflow 6.  Discuss the use of unit testing in the context of model production 7.  Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

  Langue d’origine

  540 min

  Voir les détails: AI Workflow: AI in Production
• Technologie

  Avancé

  AI Workflow: Machine Learning, Visual Recognition and NLP

  This is the fourth course in the IBM AI Enterprise Workflow Certification specialization.    You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.  Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company.  The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge.  The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks.  Out-of-the-box Watson models for natural language understanding and visual recognition will be used.  There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.   By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

  Langue d’origine

  540 min

  Voir les détails: AI Workflow: Machine Learning, Visual Recognition and NLP
• Affaires

  Intermédiaire

  Analyze and Highlight Cost Variances Effectively

  In fast-moving business environments, variance analysis is more than just reporting — it’s strategic insight. This course equips finance and operations professionals to move beyond identifying differences between actual and standard costs and instead analyze, interpret, and communicate what those variances mean for business performance. You’ll calculate price, quantity, and mix variances using standard cost formulas, apply these concepts to real-world data templates, and learn to spotlight unfavorable variances that demand management attention. By the end, you’ll be ready to transform variance data into actionable intelligence for cost control and forecasting.

  Langue d’origine

  Voir les détails: Analyze and Highlight Cost Variances Effectively
• Technologie

  Débutant

  Troubleshooting with AI Using Cloud Assist Investigations

  Cloud Assist Investigations is an AI-enabled tool for iterative troubleshooting in Google Cloud environments, and aims to help users troubleshoot common issues to find the root cause of problems that may occur when using Google Cloud services. This course equips DevOps, SRE, and platform engineers with the skills to effectively use Cloud Assist Investigations for streamlined troubleshooting of Google Cloud services. The training focuses on foundational knowledge of Cloud Assist Investigations, with the intent to reduce troubleshooting time and enhance overall operational efficiency for users of Google Cloud.

  Langue d’origine

  Voir les détails: Troubleshooting with AI Using Cloud Assist Investigations
• Affaires

  Débutant

  Risk governance: Manage the risks

  All organisations must grapple with fundamental issues at the heart of governance: who are our stakeholders? What are their objectives? How can we ensure these objectives are met as effectively as possible? How can we ensure all legitimate stakeholders are treated fairly? Via structured learning activities (video lectures, quizzes, discussion prompts and written assessments) this course will teach you how to address these questions and how a sound governance structure and comprehensive risk management framework can support you and your organisation in achieving its objectives. You will consider contemporary ethical issues and devise practical responses to them, and finally, you’ll discover that your future ‘influencing’ challenge will be to encourage all members of your organisation to understand their role in serving your organisation’s stakeholders.

  Langue d’origine

  1440 min

  Voir les détails: Risk governance: Manage the risks
• Général

  Tous les niveaux

  Initiation à la théorie des distributions

  Une fonction discontinue peut-elle être solution d'une équation différentielle? Comment définir rigoureusement la masse de Dirac (une "fonction" d'intégrale un, nulle partout sauf en un point) et ses dérivées? Peut-on définir une notion de "dérivée d'ordre fractionnaire"? Cette initiation aux distributions répond à ces questions - et à bien d'autres.

  Langue d’origine

  Voir les détails: Initiation à la théorie des distributions
• Technologie

  Intermédiaire

  Google Cloud でのバッチデータ パイプラインの構築

  この中級コースでは、Google Cloud で堅牢なバッチデータ パイプラインを設計、構築、最適化する方法を学習します。基本的なデータ処理から一歩進んで、大規模なデータ変換と効率的なワークフロー オーケストレーションを確認します。この内容は、タイムリーなビジネス インテリジェンスと重要なレポートの作成に不可欠です。 実装に Apache Beam 用の Dataflow と Apache Spark 向け Serverless（Dataproc Serverless）を使用する実践的な演習を行い、パイプラインの信頼性の確保と効果的な運用を実現するために、データの品質、モニタリング、アラートに関する重要な考慮事項に対処します。データ ウェアハウジング、ETL / ELT、SQL、Python、Google Cloud のコンセプトに関する基本的な知識があることが推奨されます。

  Langue d’origine

  Voir les détails: Google Cloud でのバッチデータ パイプラインの構築
• Technologie

  Intermédiaire

  Serverless Data Processing with Dataflow: Foundations em Português Brasileiro

  Este é o primeiro de uma série de três cursos sobre processamento de dados sem servidor com o Dataflow. Nele, vamos relembrar o que é o Apache Beam e qual é a relação entre ele e o Dataflow. Depois, falaremos sobre a visão do Apache Beam e os benefícios do framework de portabilidade desse modelo de programação. Com esse processo, o desenvolvedor pode usar a linguagem de programação favorita com o back-end de execução que quiser. Em seguida, mostraremos como o Dataflow permite a separação entre a computação e o armazenamento para economizar dinheiro. Além disso, você vai aprender como as ferramentas de identidade, acesso e gerenciamento interagem com os pipelines do Dataflow. Por fim, vamos ver como implementar o modelo de segurança ideal para seu caso de uso no Dataflow.

  Langue d’origine

  1260 min

  Voir les détails: Serverless Data Processing with Dataflow: Foundations em Português Brasileiro