Parameterization Courses and Certifications

Automation Step by Step

AccelQ Beginner Tutorials - Real-World Examples Parameterization - How to Parameterize Date Field

Learn AccelQ parameterization techniques for data-driven testing, including date fields. Practical examples and tips for creating flexible, reusable test cases with parameterized actions and test data.

• YouTube
• 21 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Machine Learning Based Parameterization in Monsoon Dynamics

Explore advanced machine learning techniques for parameterizing atmospheric processes, focusing on monsoon dynamics, convective organization, and weather modeling applications.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Parameterization Invariant Representations for Efficient Shape Learning

Explore parameterization invariant representations for 3D graphs and meshes, focusing on varifold norm gradients to enable robust neural network processing of raw scan data.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Robot Framework with Python- Selenium/API Automation Testing

6342 ratings at Udemy

Build Selenium UI & API Tests in fastest & easiest way using Robot Framework from this Step by step tutorial

• Udemy
• 8 hours 44 minutes
• On-Demand
• Paid Course

Learn Cucumber BDD Framework

1181 ratings at Udemy

Mastering BDD Cucumber for Selenium with Java JUnit

• Udemy
• 3 hours 49 minutes
• On-Demand
• Free Online Course

Azure Data Factory Training--Continuous Integration/Delivery

954 ratings at Udemy

Master Azure Data Factory: Build Real Projects with CI/CD, DevOps & Pipelines – Kickstart Your Data Engineering Career

• Udemy
• 20 hours 2 minutes
• On-Demand
• Paid Course

Load Testing Using HP LoadRunner 12

566 ratings at Udemy

In this course we will use HP LoadRunner 12 to load test a web application.

• Udemy
• 10 hours 53 minutes
• On-Demand
• Paid Course

Professor Leonard

Calculus 3 - Arc Length/Parameterization, TNB (Frenet-Serret) Intro

Explore arc length, parameterization, and Frenet-Serret frames in vector functions. Learn to calculate, reparameterize, and understand movable frames in space curves.

• YouTube
• 2 hours 13 minutes
• On-Demand
• Free Video

Complete M Language from Scratch in Power Query for Power BI

99 ratings at Udemy

Complete M Language Training: From Beginner to Advanced in Power Query for Power BI.

• Udemy
• 6 hours 55 minutes
• On-Demand
• Paid Course

Mobile App Performance Testing Mastery: JMeter & LoadRunner

36 ratings at Udemy

Optimize, Test, and Analyze Mobile Apps for Peak Performance

• Udemy
• 1 day 1 hour 38 minutes
• On-Demand
• Paid Course

Coursera

Scaling No-Code Automation with Reflect

Master advanced no-code testing with Reflect to build resilient, scalable automation for dynamic web applications without writing code.

• Coursera
• 3 hours 32 minutes
• On-Demand
• Paid Course

Calculus 1C: Coordinate Systems & Infinite Series

Dive into coordinate systems, curve calculus, and infinite series, mastering Taylor polynomials and convergence properties in this MIT calculus course.

• MITx Online
• 6 weeks, 5-10 hours a week
• On-Demand
• Free Online Course

Automation Step by Step

REST Assured API Testing Beginner Tutorial - Parameterization and Data-Driven Testing

Learn data-driven API testing with REST Assured, covering parameterization, TestNG DataProvider, and @Parameters annotation for efficient test execution with multiple data sets.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Kavli Institute for Theoretical Physics

A Deep Learning Parameterization of Gravity Wave Drag Coupled to an Atmospheric Global Climate Model - Aditi Sheshadri

Explores deep learning parameterization of gravity wave drag in climate models, demonstrating improved predictions of atmospheric phenomena like the QBO and potential for generalizing to future climate scenarios.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Kavli Institute for Theoretical Physics

Lessons and Outlook for ML Parameterization of Sub Grid Atmospheric Physics From the Vantage of Emulating Cloud Superparameterization - Mike Pritchard

Exploring machine learning for atmospheric physics parameterization, focusing on cloud superparameterization emulation. Discusses challenges, strategies, and lessons learned in applying ML to climate modeling.

• YouTube
• 42 minutes
• On-Demand
• Free Video