COMPUTER FUNDAMENTALS

Detailed explanation-1: -Machine learning algorithms build a model based on sample data, known as “training data, ‘’ in order to make predictions or decisions without being explicitly programmed to do so. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Detailed explanation-2: -Training data is the data you use to train an algorithm or machine learning model to predict the outcome you design your model to predict. If you are using supervised learning or some hybrid that includes that approach, your data will be enriched with data labeling or annotation.

Detailed explanation-3: -Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.

Detailed explanation-4: -Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases.