Supervised learning is a paradigm in machine learning, using multiple pairs consisting of an input object and a desired output value to train a model. The training data is analyzed and an inferred function is generated, which can be used for mapping new examples.

1. Name : Ayan Das
Dept : CSE
Year : 4th
Subject : Machine Learning
Subject Code : PEC CS 701E
Roll No : 25300121057
Topic : Supervised Learning

2. What is Machine Learning ?
 Machine Learning is a branch of artificial intelligence that develops algorithms by
learning the hidden patterns of the datasets used it to make predictions on new
similar type data, without being explicitly programmed for each task.
 Example:-
 Facial recognition
 Email automation and spam filtering

3. There are primarily three types of machine learning
 Supervised
 Unsupervised
 Reinforcement Learning.
Types Of Machine Learning

4.  Supervised learning is the types of machine learning in which machines are
trained using well "labelled" training data, and on basis of that data, machines
predict the output. The labelled data means some input data is already tagged with
the correct output.
 In supervised learning, the training data provided to the machines work as the
supervisor that teaches the machines to predict the output correctly. It applies the
same concept as a student learns in the supervision of the teacher.
Supervised Learning

5. Supervised learning process:two steps
 Learning (training): Learn a model using the training data
 Testing: Test the model using unseen test data to assess the model
accuracy
 Accuracy= No. of correct classifications / Total no of test cases
Training
data Learning
algo
accuracy
model Test the
data
Training part Testing part

6. Supervised learning can be further divided into two types of problems:
1. Regression
2. Classification
Regression: A regression problem is when the output variable is a real or continuous
value
Classification: Classification algorithms are used when the output variable is
categorical, which means there are two classes such as Yes-No, Male-Female, True-
false, etc.
Types of Supervised Learning

7. Overview
 In order to solve a given problem of supervised learning, one has to perform the following steps:
1. Determine the type of training examples. Before doing anything else, the user should decide
what kind of data is to be used as a training set.
2. Gather a training set. Thus, a set of input objects is gathered and corresponding outputs are
also gathered, either from human experts or from measurements.
3. Determine the structure of the learned function and corresponding learning algorithm.
4. Complete the design. Run the learning algorithm on the gathered training set.
5. Evaluate the accuracy of the learned function. After parameter adjustment and learning, the
performance of the resulting function should be measured on a test set that is separate from the
training set.