Nss power point_machine_learning

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Can these terms be used interchangeably
Deep learning is a
subset of Machine
learning and
machine learning is
a subset of AI

Machine Learning:
1. Supervised Learning
2. Unsupervised Learning (Clustering)
3. Data Mining
4. Statistical Analysis
5. Predictive Analysis
Deep learning:
1. Speech Recognition
2. Natural Language Processing
3. Image Processing
4. Computer Vision
5. Board Games
AI:
1. Self driving Cars
2. Chat bots
3. Robots (Sophia)
4. Auto Pilot
Introduction

Goals of the course:
1) Identify a machine learning problem.
2) Use basic machine learning techniques
3) Think about your data/results.
What is Machine Learning?
Machine Learning explores the construction and usage of the algorithm
that can learn from data.
Machine has the ability to learn from data, it is able to improve the
performance when it receives more information and this experience
typically comes in the form of observations on how particular instances
on a problem was solved before.
1) Constructs/ use algorithms that learn from data
2) More information Higher performance
3) Previous Solutions Experience

Learns from experience Learns from Data

Problem
Statement
Example
Label squares: size and edge Color
Earlier observations (Labeled by humans):
Task for computer = label unseen square:
?
Result: right or wrong!

Input Knowledge:
In example: pre-labeled squares
Size Edge Color
Small Dotted Green
Big Striped Yellow
Medium Normal Green
Features
Label
ObservationsIn R – use data.frame()
> Squares<- data.frame(
size = c(“small”, “big”, “medium”),
edge = c(“dotted”, “striped”, “normal”),
color = c(“green”, “yellow”, “green”))

Class Functions :
In a data frame rows corresponds to observations and column corresponds to
variables.
To find out the dimensions of the data sets, dim function can be used.
> dim(squares) #Observations, # features
> str(squares) Structured Overview
>summary(squares) Distribution Measures

How Machine Learning works?
Machine Learning is a method in which the machine is
given the input data, on which it is trained and then tested
for predicting the output of the new inputs given.
Machine
Training
Data Learned
Machine
Test Data
New Input
Predicted
Output
Model
Accuracy
Compare
Predicted
Output
Input Data

Machine
Training
Data
Learned
Machine
Test Data
Test Data
Predicted
Output
Model
Accuracy
Compare
Predicted
Output
Train
Parameter A Parameter B Output
2 3 5
4 7 11
3 6 9
5 6 11

Parameter A Parameter B Output
2 3 5
4 7 11
3 6 9
5 6 11
Predicted
Output - Actual
Output --> Confusion
Matrix

Data Set
Cleaning
data Set
•The Data needs to be cleaned in the
format which is suitable for performing
machine learning and getting the desired
output.
Data
Shuffling
•This step is very important
and will be discussed in
further slides.
Divide the
data in two
parts
•The data needs to be
divided into training and
testing data set.
Categorizing
the inputs
and the
output
•The output can be generally of
two types i.e. Classification
and Regression.
Input the
training data set
into the
Machine
•By providing the training data set
to the machine, we are getting
the model ready for prediction.
Providing inputs of test
data to the model and
getting the output.
Comparison of predicted
output and test dataset
output.
•This will help us to
get the model
accuracy.
The model is now
ready for prediction.
Machine Learning Work Flow:

Cross
Validation:
-Example 4 fold cross validation

How to choose input parameters?
and
Factors to be considered while selecting

Correlation:-

Interactions:-
When variables are brought together to get a predictive output, which may behave different individually, but gives
different output when put together.
For Example, Having Alcohol while driving can lead to an accident. Also using mobile phone can also lead to an
accident. But driving using mobile phone when drunk will have an additive effect.
On the contrary, when drug A and drug B taken individually, might be harmful but taken together can
nullify each others effect.

Automatic feature Selection:-
There are possibilities, when we did not know which predictors to select as we
cannot judge the impact of them on the prediction (might be due to lack of subject
matter knowledge).
In this case, we use a method called stepwise regression.
There are two types of Stepwise regression:-

Forward Stepwise:
Even though the two models show the same output, this is not always the case.
It is possible that the two could come to completely different conclusions about the most important predictors.

The two can create completely different models. This does not guarantee that which one is the better.
Statisticians raise concerns that the stepwise regression model violates some of the principles that allows the
regression model to explain data as well predict.
But using stepwise regression does not mean that the models predictions are worthless. It simply means that
the model may over or under state the importance of predictors.

Dummy Variables, Missing data and interactions:-
Dummy Variables:-
All the predictor used in regression analysis must be numeric.(That is categorical data should be represented in
numbers.)
Missing Data:-
Missing data creates problem while doing predictive analysis in creating a model. As it should be replaced by a value,
that can be used for predictive analysis.
Good 1
Bad 0
NA/Bank Mean()
NA Sd()
NA 0

Machine
Learning
Problem
statements
There are three types of machine learning problems:
- Classification
- Regression
- Clustering
Classification problem:
Goal: Predict the category of new Observation.
Estimate
Earlier observations Classifier
Classifier
Unseen Example Class

Classification-
Applications
Common Applications of Classification:
1. Medical Diagnosis
2. Animal Recognition
3. ?
Important:
 Qualitative Output
 Predefined Classes
The classes in Medical diagnosis may be:
 Sick
 Very Sick
 Normal
 Heathy
The classes for Animal Recognition might be:
 Cats
 Dogs
 Horses

Module from Self driving car:

Regression
Regression Problems:
Predictors Response
• Relationship: Height – Weight?
• Linear?
• Predict: Weight Height
Regression
Function

Regression Model:
Fitting a linear Function
Predictor: Weight
Response: Height
Coefficient:
Estimated on previous input-Output
lm(response ~ predictor)

Regression -
Applications
• Payments Credit Scores
• Time Subscriptions (YouTube)
There are two things in common in above models:
 Quantitative Output
 Previous Input-Output Observations

Predicting
score

Clustering
• Clustering : grouping objects
• Similar within cluster
• Dissimilar between Clusters
• Example: Grouping similar animal photos
 No Labels
 No right or wrong
 Plenty possible clustering

Clustering -
Applications
1. Image classification
2. Pattern Recognition
3. Object Recognition
4. Recommendation engines
5. Market segmentation
6. Social network analysis
7. Search result grouping
8. Medical imaging
9. Image segmentation
10. Anomaly detection

Clustering

Classification
Regression
Clustering
Similar
Supervised Learning:
1. Predefined Labels.
2. Function Predicts label or value for new output.
3. Measuring performance is easier, as there are predicted and ad real labels
for comparison.
Un-Supervised Learning:
1. Does not require labeled observations.
2. Clustering: Finds group of observations that are similar.
3. Techniques to access such model will be discussed further.
Supervised Vs Unsupervised:

Methods or Algorithms:
Classification:
• Linear Classifiers: Logistic Regression, Naive Bayes
Classifier.
• Support Vector Machines.
• Decision Trees.
• Boosted Trees.
• Random Forest.
• Neural Networks.
• Nearest Neighbor.

Regression:
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Stepwise Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
Clustering:
• K Means
• Affinity Propagation
• Hierarchical Clustering
• Partitioning clustering
• Hybrid Clustering

Decision
Tree
Example

Decision Tree
Split the complex decision into small decisions applying the if…..else… condition to get the desired output.
For example, A bank wants to make a decision whether or not to give loan to a particular person based on his
credit score.
In this case, the bank collects all the information like income, credit score, required loan amount, etc. Here the
bank must quickly decide whether or not to give loan.

On the basis of the information given, we can create a decision tree using the Divide-and –Conquer method.
This method will help to analyze whether the customer will be able to repay the loan amount or not.
Suppose the tree considers two aspects of each applicant the credit score and the requested loan amount
The algorithm looks for an initial split that creates the two most homogeneous groups.
First, it splits into groups of high and low credit scores, then it splits requested the loan amount into high and
low. Each of these splits results into if…else… decision in a tree structure mentioned below:

If credit score is low it predicts loan default and if it is high and the loan amount is large it will predict default
as well, otherwise it predicts repaid.
The scenarios will be much more complex than this. This illustrates the basic process of how such a tree might
be built.
One the most widely used package is “rpart”.
(Formula)
Method=classification tree.

See You
Soon !!

Nss power point_machine_learning

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