ISTQB AI Testing Learn best practices and prepare for exam

ISTQB AI Testing – Learn best practices and prepare for exam, available at $89.99, has an average rating of 4.41, with 41 lectures, based on 776 reviews, and has 4545 subscribers.

You will learn about Understand the current state and expected trends of AI. Experience the implementation and testing of a ML model and recognize where testers can best influence its quality. Understand the challenges associated with testing AI-Based systems, such as their self-learning capabilities, bias, ethics, complexity, non-determinism and more Contribute to the test strategy for an AI-Based system. Design and execute test cases for AI-based systems. Recognize the special requirements for the test infrastructure to support the testing of AI-based systems. Understand how AI can be used to support software testing. This course is ideal for individuals who are Anyone involved in testing AI-based systems and/or AI for testing. This includes people in roles such as testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers. This certification is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants. To gain this certification, candidates must hold the Certified Tester Foundation Level certificate. It is particularly useful for Anyone involved in testing AI-based systems and/or AI for testing. This includes people in roles such as testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers. This certification is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants. To gain this certification, candidates must hold the Certified Tester Foundation Level certificate.

Enroll now: ISTQB AI Testing – Learn best practices and prepare for exam

Summary

Title: ISTQB AI Testing – Learn best practices and prepare for exam

Price: $89.99

Average Rating: 4.41

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the current state and expected trends of AI.

Experience the implementation and testing of a ML model and recognize where testers can best influence its quality.

Understand the challenges associated with testing AI-Based systems, such as their self-learning capabilities, bias, ethics, complexity, non-determinism and more

Contribute to the test strategy for an AI-Based system.

Design and execute test cases for AI-based systems.

Recognize the special requirements for the test infrastructure to support the testing of AI-based systems.

Understand how AI can be used to support software testing.

Who Should Attend

Anyone involved in testing AI-based systems and/or AI for testing. This includes people in roles such as testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers. This certification is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants. To gain this certification, candidates must hold the Certified Tester Foundation Level certificate.

Target Audiences

Anyone involved in testing AI-based systems and/or AI for testing. This includes people in roles such as testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers. This certification is also appropriate for anyone who wants a basic understanding of testing AI-based systems and/or AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants. To gain this certification, candidates must hold the Certified Tester Foundation Level certificate.

Course Overview

The testing of traditional systems is well-understood, but AI-based systems, which are becoming more prevalent and critical to our daily lives, introduce new challenges. This course will introduce the key concepts of Artificial Intelligence (AI), how we decide acceptance criteria and how we test AI-based systems. These systems have unique characteristics, which makes them special – they can be complex (e.g. deep neural nets), self-learning, based on big data, and non-deterministic, which creates many new challenges and opportunities for testing them.

The course will introduce the range of types of AI-based systems in use today and explain how machine-learning (ML) is often a key part of these systems and show how easy it is to build ML systems. We will look at how the setting of acceptance criteria needs to change for AI-based systems, why we need to consider ethics, and show how the characteristics of AI-based systems make testing more difficult than for traditional systems.

Introduction to ISTQB AI Testing Course by AIT

Three perspectives are used to show how quality can be achieved with these systems. First, we will consider the choices and checks that need to be made when building a machine-learning system to ensure the quality of data used for both training and prediction. Ideally, we want data that is free from bias and mis-labelling, but, most importantly, closely aligned with the problem. Next, we will consider the range of approaches suitable for the black-box testing of AI-based systems, such as back-to-back testing and A/B testing, introducing, in some detail, the metamorphic testing technique. Third, we will show how white-box testing can be applied to drive the testing and measure the test coverage of neural networks.

The need for virtual test environments will be demonstrated using the case of self-driving cars as an example.

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Finally, the use of AI as the basis of tools that support testing will be considered by looking at examples of the successful application of AI to common testing problems.

The course is highly practical and includes many hands-on exercises, providing attendees with experience of building and testing several different types of machine learning systems. No programming experience is required.

Course Curriculum

Chapter 1: Introduction to AI

Lecture 1: intro_to_course

Lecture 2: Intro to AI Part 1

Lecture 3: Intro to AI Part 2

Chapter 2: Quality Characteristics for AI-Based Systems

Lecture 1: Quality Characteristics Part 1

Lecture 2: Quality Characteristics Part 2

Lecture 3: Quality Characteristics Part 3

Chapter 3: Machine Learning (ML) – Overview

Lecture 1: ML Overview

Lecture 2: Exercise 1a

Lecture 3: Exercise 1b

Lecture 4: ML Types

Lecture 5: ML Workflow Part 1

Lecture 6: ML Workflow Part 2

Chapter 4: ML – Data

Lecture 1: Data Prep and Acquisition

Lecture 2: Exercise 3a

Lecture 3: Data Pre_Processing

Lecture 4: Exercise 3b

Lecture 5: Data Prep Challenges

Lecture 6: Exercise 3c

Lecture 7: Data Quality

Lecture 8: ML Workflow

Chapter 5: ML Functional Performance Metrics

Lecture 1: Perf Metrics Confusion Matrix

Lecture 2: Perf Metrics Beyond

Chapter 6: ML – Neural Networks and Testing

Lecture 1: Intro to Perceptrons

Lecture 2: Exercise 9

Lecture 3: Neural Networks

Chapter 7: Testing AI-Based Systems Overview

Lecture 1: Specification and Oracle Problem

Lecture 2: Acceptance Criteria and Documentation

Lecture 3: Test Levels for AI

Chapter 8: Testing AI-Specific Quality Characteristics

Lecture 1: AI Specific Testing Issues Part 1

Lecture 2: AI Specific Testing Issues Part 2

Chapter 9: Methods and Techniques for the Testing of AI-Based Systems

Lecture 1: Selecting Test Approaches

Lecture 2: M_0T Intro and Attacks

Lecture 3: Combinatorial Testing

Lecture 4: B2B Testing

Lecture 5: Metamorphic Testing

Lecture 6: Experience-Based Testing

Chapter 10: Test Environments for AI-Based Systems

Lecture 1: Test Environments

Chapter 11: Using AI for Testing

Lecture 1: Using AI Part 1

Lecture 2: Using AI Part 2

Lecture 3: Using AI Part 3

Lecture 4: Exercise 18

Instructors

STA consulting
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Rating Distribution

1 stars: 3 votes

2 stars: 15 votes

3 stars: 89 votes

4 stars: 293 votes

5 stars: 376 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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