Q. What is Machine Learning?
Ans. Machine Learning is a branch of Artificial Intelligence that enables computers to learn patterns from data and make predictions or decisions.
Q. What is the main objective of Machine Learning?
Ans. The main objective is to enable computers to learn from examples and improve their performance on specific tasks.
Q. What is the relationship between AI and Machine Learning?
Ans. Artificial Intelligence is the broader field, while Machine Learning is a subset of AI that learns patterns from data.
Q. What is a Machine Learning model?
Ans. A Machine Learning model is a mathematical or computational system trained on data to make predictions or identify patterns.
Q. What is training data?
Ans. Training data is the information used by a Machine Learning algorithm to learn patterns and relationships.
Q. What is testing data?
Ans. Testing data is a separate portion of data used to evaluate how well a trained model performs on previously unseen examples.
Q. What is supervised learning?
Ans. Supervised learning trains a model using labelled examples that contain input data and the expected output.
Q. What is unsupervised learning?
Ans. Unsupervised learning discovers patterns, groups or structures in data without predefined labels.
Q. What is reinforcement learning?
Ans. Reinforcement learning trains an agent through interaction with an environment using rewards or penalties.
Q. What is classification in Machine Learning?
Ans. Classification is a task in which a model assigns an input to a category, such as spam or non-spam.
Q. What is regression in Machine Learning?
Ans. Regression predicts a numerical value, such as a house price or tomorrow’s temperature.
Q. What is clustering?
Ans. Clustering groups similar data points together without requiring predefined category labels.
Q. What is a feature in Machine Learning?
Ans. A feature is an input variable or measurable property used by a model to make predictions.
Q. What is a label in supervised learning?
Ans. A label is the known target or expected output associated with a training example.
Q. What is overfitting?
Ans. Overfitting occurs when a model learns the training data too closely, including noise, and performs poorly on new data.
Q. What is underfitting?
Ans. Underfitting occurs when a model is too simple to learn the important patterns in the data.
Q. What is an algorithm in Machine Learning?
Ans. An algorithm is a set of computational steps used to learn patterns from data or train a model.
Q. Give two real-life applications of Machine Learning.
Ans. Examples include email spam detection and recommending videos or products based on user preferences.
Q. Why is data quality important in Machine Learning?
Ans. Good-quality data helps models learn useful patterns and produce more reliable predictions.
Q. What are the limitations of Machine Learning?
Ans. Machine Learning can require large amounts of suitable data and computing resources, may produce incorrect predictions, and can inherit bias from its training data.