Certified Tester in Artificial Intelligence
AI is ubiquitous today; it is a hot topic in the technology space as well as increasing its inception in other realms such as health care, shopping, gaming, and other business. Artificial Intelligence United (AIU) was created to support the understanding of the implementation of these important advancements. AiU-Certified Tester in Artificial Intelligence (AiU-CTAI) is a 3-day practical certification course, which goes beyond the fundamentals of Artificial Intelligence and Machine Learning and helps discover testing in this growing world… It improves user experience, reliability, consistency, and predictability with quality. Join the Artificial Intelligence community today and enjoy the journey.
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Format
Instructor-Led
online Program
Start Date
Instructor-Led
program on
Dec 09-11, 2022
About the Course
Why should you go for AiU-Certified Tester in Artificial Intelligence(CTAI)
AiU-Certified Tester in Artificial Intelligence(CTAI) is a framework that helps understand the current trends, industry application using Artificial Intelligence (AI) through the application of Machine Learning (ML). Participants will be able to decide the best algorithms for ML issues and categorize on the ML mechanisms. This course boosts your confidence in implementing the latest testing strategies in your organization.
Course Outline
Chapter 1: Introduction to Artificial Intelligence
- 1.1 Artificial Intelligence (AI) and Machine Learning (ML)
- 1.1.1 Defining Artificial Intelligence (AI)
- 1.1.2 Types of AI
- 1.2 Types of ML
- 1.2.1 Supervised learning – Classification and Regression
- 1.2.2 Unsupervised learning – Clustering and Association
- 1.2.3 Reinforced Learning
- 1.2.4 Deep Learning (DL) and types of Neural Networks (RNN, DNN, CNN)
- 1.3 Stages of ML Process
- 1.3.1 Stages of ML Process – CRISP-DM process
- 1.3.2 Steps for Identification of ML problem type
Chapter 2: Overview of Testing AI Systems
- 2.1.1 Offline and Online testing of AI Systems
- 2.2.1 AI testing Vs traditional testing
- 2.3.1 Quality characteristics for evaluating AI systems
- 2.3.2 Extended Quality characteristics specific to AI
Chapter 3: Offline Testing of AI Systems
- 3.1 Data preparation and preprocessing
- 3.1.1 Steps of data preparation and preprocessing
- 3.1.2 Data manipulation and filtering
- 3.1.3 Processing of Unstructured data (images)
- 3.1.4 Processing of Unstructured data (text)
- 3.1.5 Dimensionality Reduction
- 3.1.6 Data visualization
- 3.1.7 Anomaly/Outliers detection
- 3.1.8 Outliers Detection Techniques
- 3.1.9 Data imputation
- 3.2 Metrics
- 3.2.1 Role of metrics
- 3.2.2 Metrics for supervised and unsupervised learning
- 3.2.3 Inertia and adjusted rand score
- 3.2.4 Support, confidence and lift
- 3.2.5 Confusion matrix
- 3.2.6 Accuracy, precision, recall, specificity and F1-score
- 3.2.7 RMSE and R-Square
- 3.3.1 Training, validation and testing datasets
- 3.3.2 Underfitting and Overfitting
- 3.3.3 Cross-validation
- 3.4.1 Analytics
Chapter 4: Online Testing of AI Systems
- 4.1 Architecture of an AI application
- 4.1.1 Components of an intelligent app and their testing needs
- 4.1.2 Interaction of AI and non-AI parts
- 4.2 Linguistic analysis method
- 4.2.1 Linguistic analysis based test design
- 4.3 Testing AI systems
- 4.3.1 Test a chatbot
- 4.3.2 Testing an image-classifier
Chapter 5: Explainable AI
- 5.1.1 Explainable AI and its need
- 5.1.2 LIME
- 5.1.3 CAM for Neural Networks
- 5.1.4 Counterfactual examples
Chapter 6: Risks and Test Strategy for AI Systems
- 6.1 Risks in testing AI
- 6.1.1 Challenges of testing AI systems
- 6.1.2 Risk of using pre-trained models
- 6.1.3 Risk of Concept Drift (CD)
- 6.1.4 Challenges of AI test environment
- 6.2 Test Strategy
- 6.2.1 Test Strategy for Testing AI applications
Chapter 7: AI in Testing
- 7.1 AI for STLC
- 7.1.1 AI for Software Testing Lifecycle methods
- 7.1.2 AI for smart reporting and dashboards
- 7.2 AI based automation tools
- 7.2.1 Tools
Certifications
Know your coach
Viepul Kocher
President of Indian Testing Board (ISTQB), Soul of testAIng.com (tAI) & CEO, Verity Software
About the Instructor
Viepul Kocher is an ex-Adobe engineer and IIT alumnus with a 25-year experience in Software Development and Testing industry. Besides being the creator of AiU – the world’s first AI testing certification, DoU – DevOps united and others, Viepul is also President of the Indian Testing – ISTQB Board, Convener of STeP-IN forum and National Convener of Indic Academy, to name a few.
He is the founder of TestAIng – world’s first AI focused testing services company and Verity software – a training company. Viepul co-founded and ran PureTesting, a hugely successful and recognized testing services company from 2004 to 2012 and sold it to start SALT (School of Applied Learning in Testing) – a skill assessment and upskilling company.
Tools Covered
FAQ’s
This is a 3 full day course which will start at 09:30 AM and end at 05:30 PM on all the 3 days.
No. We do not provide any tools with this course.
Yes this certification is valid for lifetime.
No, we do not provide placement.
The maximum batch size is 20 members in one batch.
Yes post training support will be provided, you can contact the trainers for any queries which you may have.
No, coding is not a prerequisite.
We strongly recommend that the participants attend the batch which they have specifically registered for. There can be exceptions in case of emergencies but the difference in fee(if any) will have to be borne by the participant.
No we do not provide refunds upon cancellation.
Yes we can arrange for an in-house batch for your company given there are a minimum of 10 participants per batch.
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