Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners Part - 1 | Simplilearn

What’s in it for you?
Why Machine Learning?
What is Machine Learning?
Types Of Machine Learning
Machine Learning Algorithms
Linear Regression
Decision Trees
Support Vector Machine
Use Case: Classify whether a recipe is of a
cupcake or a muffin using SVM

Because Machine can drive your
car for you!!
Because Machine can unlock your
phone with your face!!
Because Machine can now detect
50 eye diseases

Nobody likes spam posts on Facebook that
annoy them into interacting with likes, shares,
comments, and other actions

This tactic, known as “Engagement Bait,” takes
advantage of Facebook’s Newsfeed algorithm
by boosting engagement in order to get greater
reach

To eliminate engagement bait, the company reviewed and categorized hundreds of thousands of posts to
train a machine learning model that detects different types of engagement bait
Facebook scroll
GIF will be
replaced
New Post
Scans the keywords and phrases
like “This” and checks the click
through rate
This is a
tag bait!
Block this
post
Data fed to the machine

Google’s DeepMind project “AlphaGO”, a computer program that plays the board game ‘GO’ has defeated the world’s number
one Go player Ke Jie

Machine learning is the science of making computers learn and act like humans by feeding data and
information without being explicitly programmed!
Ordinary System With Artificial Intelligence Machine Learning
Learns
Predicts
Improves

Define Objective
Collect Data
Prepare Data
Select Algorithm
Deploy
Predict
Test Model
Train Model
01
02
03
04
05
06
07
08

For instance, whether the stock price will increase or decrease
Do you want to predict a
category? That’s
classification!

For instance, predicting the age of a person based on the height, weight,
health and other factors
Do you want to predict a
quantity? That’s regression!

For instance, you want to detect money withdrawal anomalies
Do you want to detect an
anomaly? That’s anomaly
detection!

For instance: Finding groups of customers with similar behavior given a
large database of customer data containing their demographics and past
buying records
Do you want to discover structure
in unexplored data? That’s
clustering

Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners Part - 1 | Simplilearn

What do you understand from Measures and Dimensions?
Each field from the data source is automatically assigned a
datatype (such as string, integer) and a role (dimension or
measure)
Aggregation applied on measures is ‘Sum’ by default but you
can always change the default aggregation in the settings
Can you tell what’s happening in the
following cases?
A. Grouping documents into different categories based on the
topic and content of each document

measure)
following cases?
B. Identifying hand-written digits in images correctly

measure)
following cases?
C. Behavior of a website indicating that the site is not working
as designed

measure)
following cases?
C. Behavior of a website indicating that the site is not working
as designed
D. Predicting salary of an individual based his/her years of
experience

Types of Machine Learning
Supervised

Supervised Un-Supervised

Supervised
Reinforcement
Un-Supervised

Supervised Learning
Labeled Data
Model Training
New Data
Square
Circle
Prediction
Supervised learning is a method used to enable machines to classify/ predict objects, problems or
situations based on labeled data fed to the machine
Circle
Square
Triangle
Labels

Unsupervised Learning
Unlabled Data Output
In Unsupervised learning, Machine Learning model finds the hidden pattern in an unlabeled data
Model Training

Reinforcement Learning
Reinforcement learning is an important type of Machine Learning where an agent learns how to behave in an
environment by performing actions and seeing the results
ACTION
NEW STATE
Agent
Environment

Supervised VS Unsupervised
No feedback
Find hidden structure
in data
Supervised
vs
Unsupervised
Labeled Data
Direct feedback
Predict output
Non-labeled data

Linear Regression
Decision Trees
Machine Learning Algorithms
There are many interesting Machine Learning algorithms, let’s have a look at a few of them

Linear Regression
y = mx + c
Linear regression is a linear model,
e.g. a model that assumes a linear relationship between
the input variables (x)
and a single output variable (y)
Linear regression is perhaps one of the most well known and well understood algorithms in statistics and
machine learning!

Linear Regression
Imagine, we are predicting distance travelled (y) from speed (x).
Our linear regression model representation for this problem
would be:
y = m * x + c
Or
distance = m * speed + c
c = coefficient
m = y-intercept

Speed = 10m/s
Distance = 36 km
Time is constant

Speed = 10m/s
Distance = 36 km
Speed = 20m/s
Distance = 52 km
Time is constant

Speed = 10m/s
Distance = 36 km
Speed = 20m/s
Distance = 52 km
Speed = 30m/s Distance = ?
Time is constant

Linear Regression
Speed
Distance
y = mx + c
Distance travelled in fixed
interval of time
c = y-intercept of line
m = +ve slope of the line
As the speed increases, distance also increases, hence the variables have a positive relationship
Speed of the person

Distance is constant
Speed = 10m/s
Time = 100 s

Speed = 10m/s
Time = 100 s
Speed = 20m/s
Time = 50 s

Speed = 10m/s
Time = 100 s
Speed = 20m/s
Time = 50 s
Speed = 30m/s Time = ?

Linear Regression
Speed
Time
y = mx + c
Time taken to travel a
fixed distance
m = -ve slope of the line
As the speed increases, time decreases, hence the variables have a negative relationship
If distance is assumed to be constant, let’s see the relationship between speed and time
Speed of the person

Linear Regression
Let’s see the mathematical implementation of Linear Regression!
Suppose we have a dataset that looks like:
x y
1 3
2 2
3 2
4 4
5 3

Linear Regression
Let’s plot these points!!
1 2 3 4 5 6
1
2
3
4
5
x y
1 3
2 2
3 2
4 4
5 3
Mean(xi) = 3

Linear Regression
Let’s plot these points!!
x y
1 3
2 2
3 2
4 4
5 3
Mean(xi) = 3 Mean(yi) = 2.8
1 2 3 4 5 6
1
2
3
4
5

Linear Regression
Now, lets find regression equation to find the best fit line!
y = mx + c
To find this equation for our data, we need to find our slope (m) and coefficient
(c)

Linear Regression
y = mx + c
( x- xi ) ( y – yi )
( x- xi ) 2m =
x y x- xi y – yi ( x- xi ) 2 ( x- xi ) ( y – yi )
1 3 -2 0.2 4 -0.4
2 2 -1 -0.8 1 0.8
3 2 0 -0.8 0 0
4 4 1 1.2 1 1.2
5 3 2 0.2 4 0.4

Linear Regression
y = mx + c
( x- xi ) ( y – yi )
( x- xi ) 2m =
1 3 -2 0.2 4 -0.4
2 2 -1 -0.8 1 0.8
3 2 0 -0.8 0 0
4 4 1 1.2 1 1.2
5 3 2 0.2 4 0.4
Total = 2Total = 10

y = mx + c
( x- xi ) ( y – yi )
( x- xi ) 2m =
1 3 -2 0.2 4 -0.4
2 2 -1 -0.8 1 0.8
3 2 0 -0.8 0 0
4 4 1 1.2 1 1.2
5 3 2 0.2 4 0.4
Total = 2Total = 10
= 2/10 = 0.2
Linear Regression

Linear Regression
y = mx + c
( x- xi ) ( y – yi )
( x- xi ) 2m = = 2/10 = 0.2
y = 0.2 x + c
2.8 = 0.2 * 3 + c
2.8 = 0.6 + c
c = 2.8 - 0.6
c = 2.2
So, we can calculate the value of c
Mean values = (3, 2.8)

Linear Regression
Hence this is our regression line!
y = ( 0.2 *x ) + 2.2
1 2 3 4 5 6
1
2
3
4
5

Linear Regression
Now, let’s predict the values of y using x = {1,2,3,4,5} and plot the points!
y = ( 0.2 *x ) + 2.2
yp = (0.2 * 1) + 2.2 = 2.4
yp = (0.2 * 2) + 2.2 = 2.6
yp = (0.2 * 3) + 2.2 = 2.8
yp = (0.2 * 4) + 2.2 = 3.0
yp = (0.2 * 5) + 2.2 = 3.2
yp = Predicted values of y

Linear Regression
Plot the predicted values along with the actual values to see the difference
1 2 3 4 5 6
1
2
3
4
5
-
-
--
Error
Error
Error
Error
x y yp
1 3 2.4
2 2 2.6
3 2 2.8
4 4 3
5 3 3.2
x
y

Linear Regression
So, our goal is to reduce this error!
1 2 3 4 5 6
1
2
3
4
5
-
-
--
Error
Error
Error
Error

Linear Regression
Minimizing the Distance: There are lots of ways to minimize the distance between the line and the data points
like Sum of Squared errors, Sum of Absolute errors, Root Mean Square error etc.
We keep moving this line through the data points to make sure the best fit line has the least square distance between
the data points and the regression line

Decision Trees
Decision Tree is a tree shaped algorithm used to determine a
course of action
Each branch of the tree represents a possible decision,
occurrence or reaction

Decision Trees
We have a data which tells us if it is a good day to play golf!
Outlook Temp Humidity Windy Play Golf
Rainy Hot High FALSE No
Rainy Hot High TRUE No
Overcast Hot High FALSE Yes
Sunny Mild High FALSE Yes
Sunny Cool Normal FALSE Yes
Sunny Cool Normal TRUE No
Overcast Cool Normal TRUE Yes
Rainy Mild High FALSE No
Rainy Cool Normal FALSE Yes
Sunny Mild Normal FALSE Yes
Rainy Mild Normal TRUE Yes
Overcast Mild High TRUE Yes
Overcast Hot Normal FALSE Yes
Sunny Mild High TRUE No

Decision Trees
Let’s determine if you should play golf when the day is sunny and
windy?

Decision Trees
Suppose, we draw our tree like this!
Humidity
Normal High
Sunny
Outlook
Overcast
Rainy
Play
Don’t Play
Play
Don’t Play

Decision Trees
But, is this the right decision tree?
For that, we should calculate Entropy and Information Gain!
Entropy is the measure of randomness or ‘impurity’ in the
dataset
Entropy
It is the measure of decrease in entropy after the dataset is
split
Also known as Entropy Reduction
Information Gain
Entropy should be low!
Information Gain should be high!

Decision Trees
Let’s look at entropy!
Better quality image
will be replaced

Decision Trees
= E(5,9)
= I(5/14, 9/14)
= I(0.36, 0.64)
= -(0.36 log2 0.36) – (0.64 log2 0.64)
= 0.94
Play Golf
Yes No
9 5
Total = 14
Entropy (Play golf)
a) Entropy of target class of the dataset (whole entropy):

Decision Trees
Entropy (Play golf, Outlook)
= P(sunny) * E (3,2) + P(Overcast) * E(4,0) + P(rainy) * E(2,3)
= 5/14 * I(3,2) + 4/14 * I(4,0) + 5/14 * I(2,3)
= 0.693
Similarly, we can calculate the entropy of other
predictors like Temperature, Humidity, Windy!
Play Golf
Predictors Yes No Total
Outlook
Sunny 3 2 5
Overcast 4 0 4
Rainy 2 3 5
14

Decision Trees
Now, let’s look at Information Gain!
Gain(Outlook) = Entropy(PlayGolf) − Entropy(PlayGolf,Outlook)
= 0.940−0.693
=0.247
The information gain of the other three attributes can be calculated in the same way:
Gain(Temp) = Entropy(PlayGolf)−Entropy(PlayGolf,Temp) = 0.029
Gain(Humidity) = Entropy(PlayGolf)−Entropy(PlayGolf,Humidity) = 0.152
Gain(Windy) = Entropy(PlayGolf)−Entropy(PlayGolf,Windy) = 0.048

Decision Trees
Now, let’s build the decision tree!
We choose the attribute with largest information gain as the root node
Sunny
Outlook
Overcast
Rainy
Windy
TrueFalse
Don’t Play
Play
Play Don’t Play
Root Node
Branch Node
Leaf Nodes

Decision Trees
So, we wanted to know if it’s a good day to play golf when it’s sunny and windy!
Sunny
Outlook
Overcast
Rainy
Windy
TrueFalse
Don’t Play
Play
Play Don’t Play

Decision Trees
Uh-Oh, it’s not a good day to play golf!
You can watch a golf game at home! :D

Support Vector Machine is a widely used classification algorithm!
The idea of Support Vector Machines is simple: The algorithm creates
a separation line which divides the classes in the best possible
manner
For example, dog or cat, disease or no disease

Weight
Height
Suppose, we have labeled sample data, which tells height and weight
of males and females

How can a machine classify whether a new data point is a male or a
female?
A new data
point
Height
Weight

We draw decision lines, but if we consider decision line 1 then we will
classify it as a male
Line 1
Height
Weight

And if we consider decision line 2, then it will be a female!
Line 1 Line 2
Height
Weight

We need to know which line divides the classes correctly, but how?
Line 1 Line 2
Height
Weight

The goal is to choose a hyperplane with the greatest possible margin between the decision line and the nearest
point within the training set
Height
Line 1
Support Vectors
Distance Margin: The distance between the hyperplane and the nearest data point from either set
Weight

When we draw the hyperplanes, we observe that Line 1 has the maximum distance margin so it will classify the
new data point correctly
Height
Line 1
Result: New data point is male!
Weight
Support Vectors

Let’s understand this with the help of an example!

Problem Statement: Classifying muffin and cupcake recipes using support vector machines
VS

Let’s have a look at our dataset:
Type Flour Milk Sugar Butter Egg Baking Powder Vanilla Salt
Muffin 55 28 3 7 5 2 0 0
Muffin 47 24 12 6 9 1 0 0
Muffin 47 23 18 6 4 1 0 0
Muffin 45 11 17 17 8 1 0 0
Muffin 50 25 12 6 5 2 1 0
Muffin 55 27 3 7 5 2 1 0
Muffin 54 27 7 5 5 2 0 0
Muffin 47 26 10 10 4 1 0 0
Muffin 50 17 17 8 6 1 0 0
Muffin 50 17 17 11 4 1 0 0
Cupcake 39 0 26 19 14 1 1 0
Cupcake 42 21 16 10 8 3 0 0
Cupcake 34 17 20 20 5 2 1 0
Cupcake 39 13 17 19 10 1 1 0
Cupcake 38 15 23 15 8 0 1 0
Cupcake 42 18 25 9 5 1 0 0
Cupcake 36 14 21 14 11 2 1 0
Cupcake 38 15 31 8 6 1 1 0
Cupcake 36 16 24 12 9 1 1 0
Cupcake 34 17 23 11 13 0 1 0

Let’s have a look at our dataset:
Type Flour Milk Sugar Butter Egg Baking Powder Vanilla Salt
Muffin 55 28 3 7 5 2 0 0
Muffin 47 24 12 6 9 1 0 0
Muffin 47 23 18 6 4 1 0 0
Muffin 45 11 17 17 8 1 0 0
Muffin 50 25 12 6 5 2 1 0
Muffin 55 27 3 7 5 2 1 0
Muffin 54 27 7 5 5 2 0 0
Muffin 47 26 10 10 4 1 0 0
Muffin 50 17 17 8 6 1 0 0
Muffin 50 17 17 11 4 1 0 0
Cupcake 39 0 26 19 14 1 1 0
Cupcake 42 21 16 10 8 3 0 0
Cupcake 34 17 20 20 5 2 1 0
Cupcake 39 13 17 19 10 1 1 0
Cupcake 38 15 23 15 8 0 1 0
Cupcake 42 18 25 9 5 1 0 0
Cupcake 36 14 21 14 11 2 1 0
Cupcake 38 15 31 8 6 1 1 0
Cupcake 36 16 24 12 9 1 1 0
Cupcake 34 17 23 11 13 0 1 0
What's the difference between a
muffin and a cupcake?
Turns out muffins have more
flour, while cupcakes have more
butter and sugar

Hence, we have built a classifier
using SVM which is able to classify
if a recipe is of a cupcake or a
muffin!

Key Takeways
What is machine learning?
Classification using SVMBuilding a Decision tree
Regression-Line of best fitTypes of Machine learning

Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners Part - 1 | Simplilearn

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