What is AI?

Machines can learn facts that help them make choices with the help of artificial intelligence. The algorithms are not meant to solve a specific problem but can draw a result from the data. The main idea behind artificial intelligence is to use machine learning to teach models with lots of data and then use those models to make predictions or produce the desired output. This description is oversimplified, and almost all of Artificial Intelligence fits into it.

What is ML?

Below, we divided the machine learning approaches into the following categories depending on the learning types.

Supervised Learning

The machine learning task of learning a function that translates an input to an output based on example input-output pairs is supervised learning. The inferred process can map additional examples as the algorithm reviews the training data. Recommendations and time series prediction are traditional challenges constructed on top of classification and regression. Linear regression for regression problems is a well-known example of supervised machine learning.

Semi Supervised Learning

In semi-supervised learning, the dataset includes both labeled and unlabeled examples. The amount of unlabeled cases is substantially bigger than the number of identified ones. The purpose of a semi-supervised learning program is the same as that of a supervised learning program.

Also, with semi-supervised learning, uncertainty is added to the problem. However, when you add unlabeled samples, a big sample displays the probability distribution of the data we categorized. A learning program can use this new knowledge.

Unsupervised Learning

An unsupervised learning method seeks to develop a model that takes a feature vector x as input and turns it into another vector or a value needed to address a practical problem. Unsupervised learning works with data that does not contain labels. Our models don’t have labels that show their desired behavior because there isn’t a trustworthy reference to measure their quality.

Reinforcement Learning

Reinforcement learning is a subset of machine learning where the machine lives in an environment and can sense the state of the environment as a set of features. The goal of reinforcement learning is to learn a policy. A policy is a function that takes the feature of a state as input and produces an optimal action to perform in that state like a supervised learning model.

Reinforcement learning handles a problem when decision-making is sequential and long-term goals, such as game playing, robotics, resource management, or logistics.

Machine Learning Test Automation Example

Example: Predictive Analysis in Test Automation

Scenario: You are part of a QA team responsible for ensuring the quality of a web application. The application is complex, with frequent updates and new features. Traditional test automation strategies are proving to be time-consuming and cannot keep up with the pace of development.

Solution: Implement AI and ML algorithms to perform predictive analysis on your test data. This involves analyzing past test results to predict potential problem areas in the software before they manifest. By identifying patterns and trends in the data, the ML model can highlight which application parts are more likely to contain bugs or fail under certain conditions.

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