The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Pragmatic AI Labs
Hugging Face Hub and Ecosystem Fundamentals
Master the Hugging Face ecosystem—the leading open-source platform for machine learning. This hands-on course teaches you to discover, evaluate, and deploy pre-trained models for text, image, and audio tasks without training from scratch. You'll learn to navigate the Hugging Face Hub to find models among 500,000+ options, read model cards to make informed selections, and understand licensing for commercial use. Through practical exercises, you'll build inference pipelines using the Transformers library, process datasets efficiently with streaming for large-scale data, and deploy models across different hardware (NVIDIA GPUs, Apple Silicon, CPU). The course culminates in building a multi-modal content analyzer that classifies text sentiment, categorizes images, transcribes audio, and generates captions—demonstrating how modern ML practitioners leverage pre-trained models to solve real problems quickly. Designed for developers and data scientists who want to accelerate their ML workflows, this course provides the foundation for fine-tuning and deploying Hugging Face models in production environments. All exercises use real-world scenarios from healthcare, fintech, and media industries.
IBM
Data Analysis with R
The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.
Coursera
A Second Brain with Obsidian
In this hands-on guided project you will learn how to use the knowledge base app Obsidian. With its powerful interface, Obsidian makes it easy for anyone to structure note taking dynamics suitable for a variety of purposes: from personal journaling, to study or work notes. On top of that, one can establish links across notes and quite literally build a digital brain based on all of these connections, powered by Obsidian's Graph View.
Cisco Learning and Certifications
Implementing Basic BGP
Gain the essential skills for configuring, monitoring, and troubleshooting the Border Gateway Protocol (BGP)—the backbone of internet routing. This course offers a deep dive into BGP’s robust and scalable architecture, used by internet service providers, universities, and corporations to ensure seamless global connectivity. Unlike standard IGPs such as OSPF, IS-IS, or EIGRP, BGP enables precise policy control and interconnectivity between distinct networks, making your expertise highly valuable in enterprise and service provider settings. Learn to distinguish BGP operations from other routing protocols, discover key path attributes for optimal route selection, and gain hands-on experience with BGP neighbor session establishment. Through practical exploration of Cisco IOS commands, you’ll build critical skills in configuring BGP, monitoring routing status, and resolving common startup issues. What sets this course apart is its focus on real-world scenarios and command-line practice—empowering you to troubleshoot and maintain resilient BGP networks. By course end, you’ll be equipped to design, implement, and support BGP solutions vital to today’s internet infrastructure.
University of Cambridge
Foundations of Finance
This course provides a rigorous, but straightforward, introduction to the key concepts of financial understanding. Using real-world case studies and practitioner interviews, as well as timely knowledge checks, you will integrate your new knowledge and problem solving skills with practical application. The course will be particularly beneficial if you: engage with/need to engage with financial specialists, and want to collaborate more effectively; are self-employed or are considering self-employment; are considering a career or secondment in finance; you are interested in corporate finance, financial management, or business finance; or you are simply interested in the subject and wish to know more.
Google Cloud
Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes
This is a self-paced lab that takes place in the Google Cloud console. AutoML Vision helps developers with limited ML expertise train high quality image recognition models. In this hands-on lab, you will learn how to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).
Alfaisal University | KLD
الاعتراف بالإيراد | Revenue Recognition
تهدف الشركات إلى تحقيق أكبر قدر من الأرباح في نهاية السنة، كي تستطيع الاستمرار في السوق، وتوزيع الأرباح على المساهمين. وكي تستطيع إنجاز ذلك، لا بدّ من أن تحقق أكبر قدر من الإيرادات (Revenues) من خلال زيادة مبيعاتها، وتقليل مصروفاتها، لكن السؤال هو: متى تتم عملية تسجيل الإيرادات في الدفاتر (Revenue Recognition)؟ وهل يتم الاعتراف بكل الإيرادات (Revenue) التي تحصل عليها الشركة أو بجزء منها؟ هذه الدورة هي دورة تمهيدية؛ فهي تلقي الضوء على أساسيات الموضوع بشكل عام بهدف التعريف به وبمحاوره الأساسية التي يجب الإلمام بها. إذا كنت من المهتمين بفهم الاعتراف بالإيراد، أو كان مجال عملك يتطلب توظيف ذلك في سياق عملك، فهذه الدورة ستكون مثالية لإغناء خبرتك وتطوير مهاراتك بشكل فعال ومؤثر. حيث ستزودك هذه الدورة باطلاع واسع ودقيق على مجموعة من المحاور المتعلقة بهذا الموضوع، مثل: استعراض أكبر المشاكل التي تواجه الشركات بخصوص الاعتراف بالإيراد، فهم كيفية المعالجة المحاسبية وعمل القيد المزدوج للإيرادات في الدفاتر، التعرف على ما يلزم الشركة لتسجيل الإيرادات في الدفاتر، فهم المقصود بمصطلحي الإيرادات وتسجيلها في الدفاتر.
Edureka
Applied Analytics Engineering and Visualization with dbt
This course equips you with practical analytics engineering skills focused on preparing, transforming, optimizing, and visualizing data using dbt. You will begin by reviewing and refactoring existing dbt models to ensure consistency, remove redundant transformations, and organize logic into clean and maintainable layers. As you move forward, you will apply standardized cleaning patterns, implement reusable macros, and enforce data quality using dbt tests. You will also design and extend business KPI models that support executive-level analytics. Next, you will deepen your understanding of performance tuning by analyzing execution plans, optimizing joins and filters, and evaluating model materializations for speed, cost, and reliability. You will learn how to improve pipeline observability by interpreting dbt logs, reviewing artifacts, managing failures, and applying freshness and SLA concepts to ensure trustworthy production workflows. The final part of the course focuses on visualization and insight delivery. You will connect dbt outputs to a BI tool, configure datasets, build dashboards based on KPI models, design executive-ready reports, automate refreshes, and share insights in a way that supports data-driven decision making across the organization. With a hands-on and applied approach, the course teaches you how to standardize transformation logic, build modular KPI models, optimize performance, monitor pipeline health, integrate analytics outputs into BI platforms, and deliver insights with clarity and impact. You will develop the ability to maintain clean project organization, implement efficient transformations, and support end-to-end analytics workflows. By the end of this course, you will be able to: • Review and refactor dbt model dependencies to maintain a clean and efficient DAG • Standardize data cleaning using reusable macros and validation strategies • Build KPI models and multi-layered business transformations • Analyze query performance and apply optimization techniques • Choose and configure dbt materializations for different performance and cost requirements • Monitor and maintain pipeline reliability using logs, artifacts, and freshness rules • Connect dbt outputs to BI tools and prepare datasets for dashboarding • Build KPI dashboards and automate reporting workflows • Communicate insights effectively through well-designed reports and storytelling techniques This course is designed for analytics engineers, data engineers, BI developers, and SQL practitioners who want to deepen their skills in dbt development, reusable SQL design, data quality practices, and workflow automation. It is ideal for learners seeking to build scalable, reliable, and well documented analytics pipelines using modern engineering workflows.
Google Cloud
Responsible AI for Developers: Privacy & Safety - Deutsch
In diesem Kurs werden wichtige Themen zu Datenschutz und Sicherheit beim Einsatz von künstlicher Intelligenz vorgestellt. Dabei lernen Sie, wie Sie mit Google Cloud-Produkten und Open-Source-Tools empfohlene Vorgehensweisen im Zusammenhang mit Datenschutz und Sicherheit beim Einsatz von KI umsetzen.
Google Cloud
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation - 日本語版
このコースでは、ML の実務担当者に、生成 AI モデルと予測 AI モデルの両方を評価するための重要なツール、手法、ベスト プラクティスを身につけていただきます。モデル評価は、ML システムが本番環境で信頼性が高く、正確で、高性能な結果を確実に提供するための重要な分野です。 参加者は、さまざまな評価指標、方法論のほか、さまざまなモデルタイプやタスクにおけるそれらの適切な適用について理解を深めます。このコースでは、生成 AI モデルによってもたらされる固有の課題に重点を置き、それらの課題に効果的に取り組むための戦略を提供します。参加者は、Google Cloud の Vertex AI プラットフォームを活用して、モデルの選択、最適化、継続的なモニタリングのための堅牢な評価プロセスを実装する方法を学びます。
Packt
Vue.js 3 and Firebase for Beginners
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this beginner-friendly course, you'll learn to develop dynamic web applications using Vue.js 3 and Firebase. You'll dive into setting up the development environment, working with the Composition API, and creating a responsive UI for your project. The course covers everything from the basics of routing in Vue.js to integrating Firebase for real-time database management and user authentication. By the end of the course, you'll be capable of building and deploying a complete web application with both a user and an admin interface. As you progress, you'll learn to implement routing with nested routes, design intuitive user interfaces, and understand the Composition API to manage state in a clean, modular way. You'll also integrate Firebase, starting with its setup, connecting to the Firestore database, and adding functionality for adding, retrieving, and deleting pizzas. Additionally, the course covers the integration of Firebase authentication, enabling users to sign up, log in, and interact with the app based on their roles. This course is perfect for anyone looking to understand modern web development. Whether you're a beginner or someone with basic JavaScript knowledge, you'll benefit from the clear, step-by-step instructions and hands-on exercises. The course assumes no prior experience with Vue.js or Firebase, making it ideal for newcomers. By the end, you'll have the skills to develop fully functional web apps with authentication and real-time data.
Packt
Concurrent and Parallel Programming in Python
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the world of concurrent and parallel programming with this detailed course designed for Python developers. Begin with threading, learning how to create and manage threads, and progress to building complex systems with threading classes. Explore practical projects like creating a Wikipedia reader and a Yahoo Finance reader, mastering the use of queues, schedulers, and database integration. Advance to multiprocessing, understanding how to leverage multiple CPU cores for enhanced performance. Learn to implement multiprocessing queues and pools, handle multiple arguments, and optimize CPU-intensive tasks. Finally, delve into asynchronous programming, mastering async tasks, timeouts, and libraries. Combine asynchronous and multiprocessing techniques for robust and scalable applications. This course provides a thorough understanding of concurrent and parallel programming, preparing you to tackle real-world challenges and optimize your Python applications for performance and efficiency. This course is ideal for Python developers, software engineers, and data scientists who want to enhance their skills in concurrent and parallel programming. A basic understanding of Python is recommended.
Johns Hopkins University
الحصول على البيانات وتنظيفها
قبل أن تتمكن من العمل مع البيانات، يجب أن تحصل على بعضها. ستتناول هذه الدورة التدريبية الطرق الأساسية التي يمكن من خلالها الحصول على البيانات. ستتناول الدورة التدريبية كيفية الحصول على بيانات من الويب ومن واجهات برمجة التطبيقات ومن قواعد البيانات ومن الزملاء بتنسيقات مختلفة. كما أنها ستتناول أساسيات تنظيف البيانات وكيفية جعل البيانات "مُرتبة". فالبيانات المرتبة تزيد من سرعة مهام تحليل البيانات النهائية. وكذلك، ستتناول الدورة مكونات مجموعة بيانات كاملة بما في ذلك البيانات الأولية وتعليمات المعالجة وكتب التعليمات البرمجية والبيانات التي تمت معالجتها. ستتناول الدورة التدريبية الأساسيات اللازمة لجمع البيانات وتنظيفها ومشاركتها.
Google Cloud
MLOps with Vertex AI: Manage Features - Español
En este curso, se presentan a los participantes las herramientas y prácticas recomendadas de MLOps para implementar, evaluar, supervisar y operar sistemas de AA de producción en Google Cloud. Las MLOps son una disciplina enfocada en la implementación, prueba, supervisión y automatización de sistemas de AA en producción. Los estudiantes obtendrán experiencia práctica con la transferencia de transmisión de Vertex AI Feature Store en la capa de SDK.
Google Cloud
App Deployment, Debugging, and Performance - 日本語版
アプリケーション デベロッパーは、このコースを通して、Google Cloud エコシステムのコンポーネントをシームレスに統合するクラウド ネイティブなアプリケーションを設計、開発する方法を学びます。講義、デモ、ハンズオンラボを通して、インフラストラクチャをコードとして扱うことによって再現可能なデプロイを作成する方法、アプリケーションに適したアプリケーション実行環境を選択する方法、アプリケーションのパフォーマンスをモニタリングする方法を学習します。 各ラボのいずれかのバージョンを完了する必要があります。各ラボは Node.js で利用できるほか、ほとんどの場合、Python または Java でも提供されます。各ラボはご希望の言語で完了できます。
University of Colorado Boulder
The Big Stuff: Evolution and Ecology
In this course, we will explore how evolution works to generate new species, the wide variety of life on earth. We will also touch on the importance of biodiversity for the overall health of our planet, and for our well being as humans. Then we will discuss ecology and the interconnectedness of life and touch on one big ecological issue in today’s society, conservation.