Introduction to Machine Learning Models (AI) Testing

Name: Introduction to Machine Learning Models (AI) Testing
Rating: 4.4 (1887 reviews)

From Scratch, Learn testing types and Strategies involved in all the phases of ML Models (AI) with real time examples

Bestseller

Created byRahul Shetty Academy - 1.2 Million QA Learners

Last updated 8/2025

English

What you'll learn

Introduction to Artificial Intelligence and Machine Learning Models
Understanding Lifecycle of Machine Learning Models and their testing Scope
Shift-Left Testing in the ML Engineering Phase such as OverFitting & UnderFitting Testing
QA Functional Testing in the ML Validation Phase with 25 different Testing types & Strategies
API Testing Scope for Machine Learning Models with ChatGPT Model example
Responsible AI Testing for Machine Learning Models such as Bias, Fairness, Ethical, Privacy Testing etc
Post-Deployment Testing Strategies for ML Models such as DataDrift & Concept Drift testing
Continuous Tracking and Monitoring Activities for QA in Production

Course content

9 sections • 39 lectures • 4h 54m total length

Introduction and Agenda of the tutorial7:13
Introduction to Artificial Intelligence Systems with examples7:57
What is Machine Learning and how it is related to Artificial Intelligence family7:53
Examples of commonly used Machine Learning Models and their usage4:12
Illustrate how ml models like ChatGPT, Gemini, and Copilot convert input into outputs using training data. Outline the ml model life cycle, qa scope, and testing across development and deployment.
Material download0:02

Understand Machine Learning Model Life cycle stages with online/offline modes5:27
How Machine Learning models works in nutshell -Learn terminologies used11:19
Understand how OverFitting Testng & UnderFitting works with Trained data sets11:04
Predicting House Prices (ML Model) Demo to show how internally Algorithms works15:34
Revision on Supervised Learning Model Testing with Overfitting/UnderFitting ex7:13
Material download0:02
Knowledge Check - Section-2

Introduction to Unsupervised Learning in the ML models with example12:17
Testing scope on Unsupervised Learning with Data point patterns&Cluster scores9:17
Explore how unsupervised learning groups data point patterns into clusters, uses silhouette scores, and performs white-box quality assurance checks with underfitting, overfitting, and validation data.
Revision on Unsupervised Learning with cluster score analysis6:01
Material download0:02
Knowledge Check - Section-3

Introduction to Reinforcement Learning in ML Model with examples10:47
Algorithms and Frameworks commonly used in developing ML Models10:41
Learn how to select algorithms for supervised and unsupervised learning, from linear and logistic regression to neural networks and clustering, using Python frameworks and testing across training, validation, and evaluation.
Material download0:02
Knowledge Check - Section-4

What are Validation Unseen Data sets and why it is required7:07
Enter the evaluation phase and apply qa practices to machine learning models, vs. web testing. Prepare unseen validation data, training data, testing data, and functional tests from business needs.
Temperature Testing to fine tune the response predictions from ML Models9:16
Prompts Testing with Zero Shot & Chain of thought Prompts test9:30
Relevance stary Testing & Fantasy claims testing on ML Models12:52
Repeatability Testing & Asking question in different phases to test7:09
Assess machine learning model reliability through repeatability testing, ensuring identical answers across repeated questions and varied phrasings, and develop diverse test cases to capture learning adaptability.
Style Transfer testing & Intent recognition testing on ML Models17:31
Explore style transfer testing and intent recognition testing for ml models, focusing on tone control, context management, and robust prompt memory.
What is Invariance Testing & BiDirectional testing for AI Models9:30
Material download0:02
Knowledge Check - Section-5

Requirements

None. All the concepts are taken care with Scratch explanation

Description

This course will introduce you to the World of Machine Learning Models Testing.

As AI continues to revolutionize industries, many companies are developing their own ML models to enhance their business operations. However, testing these models presents unique challenges that differ from traditional software testing. Machine Learning Model testing requires a deeper understanding of both data quality and model behavior, as well as the algorithms that power them.

This Course starts with explaining the fundamentals of the Artificial Intelligence & Machine Learning concepts and gets deep dive into testing concepts & Strategies for Machine Learning models with real time examples.

Below is high level of Agenda of the tutorial:

Introduction to Artificial Intelligence
Overview of Machine Learning Models and their Lifecycle
Shift-Left Testing in the ML Engineering Phase
QA Functional Testing in the ML Validation Phase
API Testing Scope for Machine Learning Models
Responsible AI Testing for ML Models
Post-Deployment Testing Strategies for ML Models
Continuous Tracking and Monitoring Activities for QA in Production

By the end of this course,
you will gain expertise in testing Machine Learning Models at every stage of their lifecycle.

Please Note:
This course highlights specialized testing types and methodologies unique to Machine Learning Testing, with real-world examples.

No specific programming language or code is involved in this tutorial.

Who this course is for:

QA Testers
Software Engineers
Software Testers
Data Engineers
Developers
Test Managers

Introduction to Machine Learning Models (AI) Testing

What you'll learn

Explore related topics

Course content

Getting Started with Machine Learning Testing basics5 lectures • 27min

Early Testing Strategies in ML Model Engineering phase (Supervised Learning)6 lectures • 51min

Unsupervised Learning Models Testing in Engineering Phase4 lectures • 28min

Reinforcement Learning & Commonly used Frameworks and Algos in ML Models3 lectures • 22min

Functional Testing for Machine Learning Models in Evaluation Phase8 lectures • 1hr 13min

Introduction to API Testing on Machine Learning Models3 lectures • 33min

Responsible AI Testing with examples on Machine Learning (AI) Models4 lectures • 25min

Post Deployment Testing Types with examples on Machine Learning Models4 lectures • 30min

Final words - Impact of Machine Learning Models in QA Space2 lectures • 6min

Requirements

Description

Who this course is for: