12,051 courses
This course introduces JAX-WS, SOAP, and XML. We'll discuss best practices and the behind the scenes details of SOAP and XML, before moving on to JAX-WS endpoints, clients, and Web Services. Through the course we'll be utilizing labs to practice and create our own projects.
This course will be useful to any programmer or data analyst who wants to expand their ability to extract knowledge from business data. You will learn how to set up a Python data science environment, particularly using Anaconda and Jupyter. This is the first course in a multi-course Specialization. All of the courses in this Specialization require that you use the provided virtual machine, which includes an installation of Python and data science libraries. The course setup instructions provided in the first module of this course go into more detail about the hardware and software requirements.
Learn how to analyze and apply disclosure requirements for interests in other entities while improving transparency and clarity in financial reporting. This course helps learners build practical skills in interpreting entity relationships, assessing control judgments, and preparing high-quality disclosures for complex ownership structures. The course begins with the foundations of entity disclosures, including purpose, scope, and the importance of reporting interests in other entities. Learners will understand how significant judgments and assumptions influence control assessments and how subsidiaries are identified and disclosed in financial statements. As the course progresses, learners examine detailed disclosure requirements related to subsidiaries, non-controlling interests, and structured entities. Practical examples help learners understand how different ownership structures affect financial reporting and how risks associated with structured entities are communicated to stakeholders. Advanced modules focus on disclosure relief provisions, joint arrangements, associates, accounting policy changes, and real-world reporting applications. Learners will strengthen their ability to analyze entity relationships and prepare transparent financial disclosures using practical, scenario-based learning. What makes this course unique is its application-focused approach to entity disclosures and reporting transparency. By the end of the course, learners will be able to interpret complex entity relationships, apply disclosure requirements confidently, assess reporting risks, and improve the quality and transparency of financial reports in accounting, finance, and audit roles.
Este curso se centrará en la optimización de Redes Neuronales Profundas, cambiando la idea de que todo el proceso es una “caja negra”. Comprenderá qué impulsa el rendimiento y podrá obtener mejores resultados de manera más sistemática Entenderá cómo optimizar los principales Hiperparámetros y su implementación. Además, aprenderá nuevos conceptos útiles para el entrenamiento de las redes como los mini-batch y las regularizaciones. También, aprenderá a implementar una red neuronal utilizando TensorFlow
À la fin de ce projet, vous aurez toutes les compétences de base pour créer une carte de visite professionnelle en utilisant la plateforme Canva, outil en ligne de création et d’édition de visuels Marketing. Vous serez capable de créer une carte de visite personnalisée en utilisant les divers outils de conception, couleurs et graphiques proposés par Canva.
By the end of this course, learners will be able to explain the foundations of Capability Maturity Model Integration (CMMI), differentiate between capability and maturity levels, apply generic goals and practices, analyze monitoring and control mechanisms, and evaluate advanced process areas for continuous organizational improvement. This comprehensive program provides a step-by-step journey through quality models, beginning with the history and framework of CMMI and advancing toward institutionalization, project management, and optimization. Learners will gain practical insights into requirements development, technical solutions, risk management, decision analysis, and organizational performance practices. Unlike traditional theory-heavy programs, this course integrates real-world scenarios with structured lessons, ensuring participants can apply concepts directly to organizational processes. What makes this course unique is its balance of foundational theory and advanced applications, coupled with structured quizzes, practical objectives, and Bloom’s Taxonomy-driven learning outcomes. Whether you are a quality manager, project leader, or aspiring professional, this course equips you with the knowledge to drive process excellence and achieve measurable improvements.
This course is the third course in the specialization about learning how to develop video games using GameMaker on Windows or Mac. Why use GameMaker instead of using C# and Unity or C++ and Unreal*? Learning to develop games with GameMaker is much easier than learning to program in C# or C++ AND learning Unity or Unreal. If you just want to continue learning how to develop games in a more "gentle" way, this is a great starting point. This course assumes you have the prerequisite knowledge from the previous courses in the specialization. You should make sure you have that knowledge, either by taking those previous courses or from personal experience, before tackling this course. Throughout this course you'll continue building your foundational GameMaker knowledge by exploring more GameMaker and game development topics. Module 1: Learn how to have NPCs plan their actions Module 2: Learn how to add HUDs (Heads-Up Displays) to your games Module 3: Learn how to implement spawners in your games Module 4: Learn how to implement a menu system * I do have Coursera Specializations called C# Programming for Unity Game Development and C++ Programming for Unreal Game Development for those more advanced topics.
Are you ready to elevate your JavaScript skills with a project-based approach? This course is designed to guide you through fundamental and advanced JavaScript concepts using practical, real-world challenges. Through hands-on projects, you'll explore functional methods such as .map() and .reduce(), key logical operations such as the ternary operator and short-circuiting, form submission, and much more. Next up, you'll explore increasingly advanced JavaScript concepts including loops, filter(), imports, and object destructuring by building three full projects; A meme picker, a cookie consent banner and an X/Twitter clone. You’ll learn to write cleaner, more efficient code and tackle challenges that solidify your understanding of JavaScript. By the end of this course, you'll have a solid understanding of powerful JavaScript methods and techniques ready to apply directly to your coding projects and in your coding career.
Collaborate and Version Control Your AI Apps is an intermediate course for AI developers, project managers, and team leads who want to move from chaotic development cycles to streamlined, professional workflows. Building great AI applications requires more than just good code; it demands effective collaboration and rigorous process management. This course provides the essential framework to align your team, manage complexity, and maintain code integrity from the first line of code to the final deployment. You will learn to master agile principles by translating high-level product requirements into clear, actionable user stories, drawing on battle-tested methods from Atlassian. Next, you will dive into the technical backbone of modern development, applying version control branching strategies like those used by top agile teams to enable parallel work and eliminate painful merge conflicts. Through hands-on exercises, AI-powered coaching, and a final project, you will build a complete toolkit for managing feature development, enhancing team transparency, and shipping better AI products, faster.
In the previous two courses, you have learned how to value startups using the discounted cash flow method and multiple methods. However, you have not learned how to estimate cash flows or earnings of startups. In this course, you are going to learn the concepts and usage of financial ratios. Using financial ratios such as profitability, liquidity, leverage, efficiency, and growth, you can tell financial health of a startup. Profitability ratios measure how profitable a firm is by looking at ROS, ROA, and ROE. Liquidity ratios measure how quickly a firm turns assets into cash to pay-off short-term liability and they include Current Ratio, Quick Ratio, and Cash Ratio. Leverage ratios measure how much long-term debt a firm has relative to its assets or equity. Efficiency ratios measure how efficiently a firm utilizes its assets. It is like a physical exam for humans and you can tell the fiscal status of a startup using financial ratios. You can also develop pro forma financial statement using financial ratios. Using pro forma financial statement, you can tell future financial status of a startup as well as cash flows. In addition, using information from pro forma statement and valuation methods, you can do the valuation of a startup.
Build production-quality command-line tools in Rust for data engineering. You move from a first hello-world CLI through real argument parsing with `clap`, ergonomic error handling with `anyhow`, and structured logging with `env_logger`. From there you learn subcommand design patterns suited to data pipelines (`ingest`, `transform`, `filter`, `export`), input validation that fails fast with a helpful message, and the data-specific flags (`--format`, `--output`, `--delimiter`, `--column`, `--limit`) every CSV and JSON tool needs. The course closes with packaging: Cargo metadata, publishing to crates.io, and a multi-stage Docker container. Along the way you learn the Rust toolchain — rustup, cargo, rust-analyzer — modules and the crates.io ecosystem, the difference between `Result` and `panic!`, and the discipline of `stderr` versus `stdout`. The capstone is `datactl`, a Rust CLI you build from scratch that reads, summarizes, filters, and exports CSV and JSON files. By the end you will have shipped a small, fast, statically-linked binary you can run anywhere.
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will train, tune, evaluate, explain, and generate batch and online predictions with a BigQuery ML XGBoost model. You will use a Google Analytics 4 dataset from a real mobile application, Flood it!, to determine the likelihood of users returning to the application. You will generate batch predictions with your BigQuery ML model as well as export and deploy it to Vertex AI for online predictions.
