Machine Learning relates with
study, design and development of models and algorithms that give computers the
capability to learn from data. Machine
learning is a core sub-area of artificial intelligence; it enables computers to
get into a mode of self-learning without being explicitly programmed. Machine
learning
explores the study and construction of algorithms that can
learn from and make predictions on data. Within
the
field of data
analytics, machine
learning is a method used to devise complex models and algorithms that lend
themselves to prediction.
Machine Learning is applied in an
incredibly wide variety of application areas where you need to make sense of
data. Machine Learning is all about
finding patterns in data. The whole idea is to let the system figure out what
it is that the person wants to do by looking at the examples (data).
Definition by Tom Mitchell:
“A Computer program is said to
learn from experience E, with respect to some task T, and some performance
measure P, if its performance on T as measured by P
improves with experience E”
e.g., An email program watches emails which emails
you do or do not mark as spam, and based on that learns how to better filter
spam.
1.Task
T – Classifying emails as spam or not spam.
2.Experience
E – Watching you label emails as spam or not spam.
3.Performance
P – The number of emails correctly classified as spam/not spam.
Two Types:
1. Supervised Learning
Where
the target variable/label is available in the data.
e.g. Linear Regression, Logistic Regression, Decision Trees, KNN, SVM, etc.
2.Unsupervised Learning
Where
there is no target variable/label to the data points.
e.g. PCA, Clustering, etc.
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