Machine Learning In Python | Python Machine Learning Tutorial | Deep Learning Python | Edureka

Agenda
✓ What is Machine Learning and its process flow
✓ Machine Learning Applications
✓ Types of Machine Learning
✓ Demo – Linear Regression

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What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) that provides computers with the ability to
learn without being explicitly programmed.
Machine learning uses data to detect patterns and create a
model and adjust program actions accordingly
How to achieve?

Machine Learning Process Flow
Using the data, the system learns an algorithm, and then uses it to build a predictive model. The system then performs
the recommended task and uses feedback data to tune the model to be more accurate.

Machine Learning Example
Example: Product Recommendation Engine
New Input
Machine Learning
Algorithm
Customer Buying History
(Data set )
Model
training
recommendfeed
1
2
3 4

Machine Learning Applications

Siri RetailHealth Care

Types Of Machine Learning
Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning

Supervised Learning
Supervised learning is where you have input variables (x) and an output variable (Y) and you use an
algorithm to learn the mapping function from the input to the output.
Y = f(X)
It is called Supervised Learning because the process of an
algorithm learning from the training dataset can be
thought as a teacher supervising the learning process

Supervised Learning Workflow
Supervised learning is the machine learning task of inferring a function from labeled training data. The training data
consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically
a vector) and a desired output value
Raw Data
Train
Labels
Feature
Extraction
Train The
Model Model Evaluate
New Data
Feature
Extraction
Predict Labels

Data Set For Supervised Learning
User ID Gender Age Estimated Salary Purchased
15624510 Male 19 19000 0
15810944 Male 35 20000 0
15668575 Female 26 43000 0
15603246 Female 27 57000 0
15804002 Male 19 76000 0
15728773 Male 27 58000 0
15598044 Female 27 84000 0
15694829 Female 32 150000 1
15600575 Male 25 33000 0
15727311 Female 35 65000 0
Label
Features

Unsupervised Learning
▪ Unsupervised learning is the training of a model using information that is neither classified nor labelled
▪ This model can be used to cluster the input data in classes on the basis of their statistical properties

Reinforcement Learning
• Reinforcement Learning is learning by interacting with a space or an environment.
• An RL agent learns from the consequences of its actions, rather than from being taught explicitly. It selects its actions
on basis of its past experiences (exploitation) and also by new choices (exploration).
Agent
Environment
Action at
R t+1
S t+1
Reward
R t
State St

Demo - Linear Regression

Housing Price Prediction
You have been hired by a real-estate company to prepare a model that can predict the housing price in
a particular area

Housing Price Prediction
In order to train the model, we will use Boston dataset. The dataset looks like this:
• CRIM - per capita crime rate by town
• ZN - proportion of residential land zoned for lots over 25,000 sq.ft.
• INDUS - proportion of non-retail business acres per town.
• CHAS - Charles River dummy variable (1 if tract bounds river; 0
otherwise)
• NOX - nitric oxides concentration (parts per 10 million)
• RM - average number of rooms per dwelling
• AGE - proportion of owner-occupied units built prior to 1940
• DIS - weighted distances to five Boston employment centres
• RAD - index of accessibility to radial highways
• TAX - full-value property-tax rate per $10,000
• PTRATIO - pupil-teacher ratio by town
• B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
• LSTAT - % lower status of the population
• MEDV - Median value of owner-occupied homes in $1000's

Linear Regression
➢ Linear Regression Analysis is a powerful technique used for predicting the unknown value of a variable
(Dependent Variable) from the known value of another variables (Independent Variable).
▪ A Dependent Variable(DV) is the variable to be predicted or explained in a regression model.
▪ An Independent Variable(IDV) is the variable related to the dependent variable in a regression
equation.

Linear Regression
Dependent variable
Y-intercept Slope of the line
Independent variable
Y = a + bX
➢ Y-intercept (a) is that value of the Dependent Variable(y) when the value of the Independent
Variable(x) is zero. It is the point at which the line cuts the y-axis.
➢ Slope (b) is the change in the Dependent Variable for a unit increase in the Independent Variable. It is
the tangent of the angle made by the line with the x-axis.

Linear Regression
Label or dependent variables
Features or Independent variables

Use-Case Implementation
Raw Data
Training Data
Testing Data
Define Features
and Labels
Train the model Evaluate
Model Calculate Accuracy

Session In A Minute
Types Of Machine Learning
Use-Case Linear Regression

WebDriver vs. IDE vs. RC
➢ Data Warehouse is like a relational database designed for analytical needs.
➢ It functions on the basis of OLAP (Online Analytical Processing).
➢ It is a central location where consolidated data from multiple locations (databases) are stored.

Machine Learning In Python | Python Machine Learning Tutorial | Deep Learning Python | Edureka

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