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• In supervised learning, the algorithm learns from labelled data, where each training example is paired with a
corresponding target label.
• There are two algorithms of supervised learning named regression and classification.
• Unsupervised machine learning algorithms are used when the information used to train is neither classified
nor labelled.
• Clustering and Dimensionality Reduction are common tasks in unsupervised learning.
• Regression is a Machine Learning algorithm used to analyse the relationship among dependent variable
(target) and independent variable (predictor). It predicts the output values based on input values.
• Regression is basically used when the dependent variable is of a continuous data type. The independent
variables, on the other hand, can be of any data type—continuous, nominal/categorical etc.
• There are several types of regression analysis, random forest regression, support vector regression, decision
tree regression, linear regression, polynomial regression, ridge regression, lasso regression and logistic
regression.
• Classification is a supervised learning concept which groups a set of data into classes.
• Classification is mainly of four types—binary classification, multiclass classification, multi-label classification,
and imbalanced classification.
• The KNN algorithm is a classifier using supervised learning and non-parametric learning (makes no assumptions
about the original data distribution) that employs closeness to classify or forecast the arrangement of a single
data point.
• Clustering, or cluster analysis, is the task of grouping a data set into similar items.
• Clustering is of four types, centroid-based clustering, density-based clustering, distribution- based clustering,
and hierarchical clustering.
• K-Means clustering is a powerful and widely used machine learning algorithm for partitioning datasets into
clusters based on feature similarity.
• The degree of association is measured by a correlation coefficient, represented by r. It is also called Pearson's
correlation coefficient and measures linear association between two variables.
• Causation shows that an event is the direct result of the occurrence of another event, i.e. a causal relationship
exists between the two events. This is also called cause and effect.
Exercise
Solved Questions
SECTION A (Objective Type Questions)
uiz
A. Tick ( ) the correct option.
1. Which of the following best describes the primary capability of machine learning?
a. It allows computer systems to acquire knowledge and improve through experience without being
specifically programmed.
b. It is a type of hardware that increases computer processing speed.
c. It involves manually coding each function and rule that the system must follow.
d. It is a method to directly control hardware using machine code.
Machine Learning Algorithms 353

