MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information.

Detailed explanation-2: -In its classical definition, ML approaches include three types of knowledge acquisition: supervised learning, unsupervised learning and reinforcement learning (3) (Figure 1).

Detailed explanation-3: -Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.

Detailed explanation-4: -Training data is also known as training dataset, learning set, and training set. It’s an essential component of every machine learning model and helps them make accurate predictions or perform a desired task. Simply put, training data builds the machine learning model.

Detailed explanation-5: -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.