Introduction to Machine Learning Models (AI) Testing

What is AI ? 

AI, or Artificial Intelligence, is the ability of a computer or machine to perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making.It uses technologies like machine learning to analyze data, recognize patterns, and act intelligently to achieve specific goals without being explicitly programmed for every single task

Examples: 

chatbots, self-driving cars, facial recognition, and smart home assistants(SIRI , Alexa)

Approaches to AI ?

• Symbolic/Rule-Based: Traditional coding practices (Expert systems, decision trees, rule engines, logic programming.) -- Legacy​

• Machine Learning: AI that learn from data,identifying patterns and relationships to make predictions or decisions without rely on rules (like Linear regression, neural networks, support vector machines, random forests.)​

• Evolutionary: Genetic algorithms, evolutionary strategies, ant colony optimization, particle swarm optimization.​

• Hybrid: Combining symbolic reasoning and neural networks like neuro-symbolic AI.

What is ML ?

Machine Learning (ML) is a subfield of AI that allows computers/machines to access data and allow them to learn for themselves instead of explicit program to perform a task, machine is trained on data and it learn patterns and relationships from data to make decisions/predictions.

Examples: seen in recommendation systems, fraud detection, image recognition, and email spam filtering (diagram below)

Netflix,youtube and other platforms

user preferences,history

Industrial settings - dat includes to predict when to maintain, repairs will be needed.