The document outlines a Python certification training program focused on data science, covering various types of machine learning: supervised, unsupervised, and reinforcement learning. It explains the definitions, approaches, training methods, and algorithms associated with each type, as well as real-world use cases such as loan approval, distance prediction, movie clustering, market basket analysis, and navigation challenges. Additionally, it highlights the importance of data warehouses for analytical needs in relation to machine learning.

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Agenda
Introduction to
Machine Learning
01 02 03
Types of Machine
Learning
Supervised vs Unsupervised
vs Reinforcement learning
04
Use Cases

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Introduction To Machine Learning

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What Is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) which provides machines the ability to learn automatically &
improve from experience without being explicitly programmed.
They look the same!
Cherry
Apple
Orange
Data
Algorithm

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Types Of Machine Learning

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Types Of Machine Learning
Reinforcement LearningSupervised Learning Unsupervised Learning

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Supervised vs Unsupervised vs Reinforcement

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Definition
Supervised learning is a
method in which we teach
the machine using
labelled data
In unsupervised learning
the machine is trained on
unlabelled data without
any guidance
In Reinforcement learning
an agent interacts with its
environment by producing
actions & discovers errors
or rewardsType of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach
Definition

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Problem Type
Regression
Classification
Supervised Learning Unsupervised Learning
Association
Clustering
Reinforcement Learning
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Type of data
Supervised Learning Unsupervised Learning Reinforcement Learning
Algorithm
Cat
CatDog
Labelled Data Unlabelled Data No Predefined Data
Algorithm
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Training
Supervised Learning Unsupervised Learning Reinforcement Learning
External supervision No supervision No supervision
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Aim
Supervised Learning Unsupervised Learning Reinforcement Learning
Forecast outcomes Discover underlying
patterns
Learn series of action
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Approach
Supervised Learning Unsupervised Learning Reinforcement Learning
Labelled Input
Known Output
Map labelled input to
known output
Understand patterns
and discover output
Follow Trail and Error
method
Unlabelled Input
Explore patterns & trends
Output
Algorithm
Training
Algorithm
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Output Feedback
Supervised Learning Unsupervised Learning Reinforcement Learning
Labelled Input
Known Output
Training
Unlabelled Input
Explore patterns & trends
Output
Input state
Environment
Reward
Direct Feedback No Feedback Reward system
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Popular Algorithms
Supervised Learning Unsupervised Learning Reinforcement Learning
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach
Linear Regression
Logistic Regression
Support Vector
Machine
K Nearest
Neighbour
Random Forest
Q- Learning
Apriori
C- Means
K- Means
SARSA

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Applications
Supervised Learning Unsupervised Learning Reinforcement Learning
Risk Evaluation
Forecast Sales
Recommendation
Systems
Anomaly Detection
Self driving cars
Gaming
Definition
Type of data
Training
Aim
Output Feedback
Popular Algorithms
Applications
Type of Problems
Approach

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Use Cases

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Use Case 1
Problem Statement: Study a bank credit dataset and make a decision about whether to approve the loan of
an applicant based on his profile
KNN algorithm
Approve loan Reject loan

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Use Case 2
Problem Statement: To establish a mathematical equation for distance as a function of speed, so you can use
it to predict distance when only the speed of the car is known.
Linear Regression
algorithm
Predict the distance, when the
speed of a car is given

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Use Case 3
Problem Statement: To cluster a set of movies as either good or average based on their
social media out reach
K-means Algorithm
Popular Movies Non-popular Movies

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Use Case 4
Problem Statement: To perform Market Basket Analysis by finding association between
items bought at the grocery store
Association rule mining &
Apriori Algorithm
Perform Market Basket Analysis

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Use Case 5
Problem Statement: Place an agent in any one of the rooms (0,1,2,3,4) and the goal is to reach
outside the building (room 5)
Q-learning Algorithm
Popular Movies
0
4
3
1
2
5
Reach room #5

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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.