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1. SUPERVISED MACHINE LEARNING
A SEMINAR PRESENTATION BY
GROUP 2

2. - CHAPTER 1 -
INTRODUCTION

3. WHAT IS SUPERVISED MACHINE LEARNING?
• SUPERVISED MACHINE LEARNING IS A SUB CATEGORY OF MACHINE LEARNING
AND ARTIFICIAL INTELLIGENCE. IT IS DEFINED BY ITS USE OF LABELED
DATASETS TO TRAIN ALGORITHMS THAT CLASSIFIES DATA OR PREDICT
OUTCOMES ACCURATELY. IT IS ALSO A LEARNING MODEL FOR PROBLEMS
WHERE THE AVAILABLE DATA CONSISTS OF LABELED EXAMPLES, MEANING THAT
EACH DATA POINT CONTAINS FEATURES AND AN ASSOCIATED LABEL. IT HAS A
PRESENCE OF A SUPERVISOR OR TEACHER WHICH IS THOUGHT TO BE THE
TRAINING DATASET.
• IN SUPERVISED MACHINE LEARNING EACH EXAMPLE CONSISTS OF AN INPUT
OBJECT (TYPICALLY A VECTOR) AND A DESIRED OUTPUT (ALSO CALLED THE
SUPERVISORY SIGNAL). THE MODEL IS TRAINED UNTIL IT CAN DETECT THE
UNDERLYING PATTERNS AND RELATIONSHIPS BETWEEN THE INPUT DATA AND
THE OUTPUT LABELS, ENABLING IT TO YIELD ACCURATE LABELING RESULTS
WHEN PRESENTED WITH NEVER-BEFORE-SEEN DATA.

4. IMPORTANCE OF SUPERVISED MACHINE LEARNING
• SUPERVISED MACHINE LEARNING PROVIDES AN EXACT IDEA ABOUT THE
CLASSES IN THE TRAINING DATA.
• SUPERVISED LEARNING IS A SIMPLE PROCESS FOR YOU TO
UNDERSTAND.
• SUPERVISED MACHINE LEARNING HELPS YOU FIND OUT EXACTLY HOW
MANY CLASSES ARE THERE BEFORE GIVING THE DATA FOR TRAINING.
• WITH SUPERVISED LEARNING, IT IS POSSIBLE FOR YOU TO BE VERY
SPECIFIC ABOUT THE DEFINITION OF THE CLASSES, THAT IS, YOU CAN
TRAIN THE CLASSIFIER IN A WAY WHICH HAS A PERFECT DECISION
BOUNDARY TO DISTINGUISH DIFFERENT CLASSES ACCURATELY.
• SUPERVISED LEARNING CAN BE VERY HELPFUL IN CLASSIFICATION OF
PROBLEMS.

5. HOW SUPERVISED MACHINE LEARNING WORKS
• SUPERVISED MACHINE LEARNING USES A TRAINING SET TO TEACH
MODELS TO YIELD THE DESIRED OUTPUT. THIS TRAINING DATASET
INCLUDES INPUTS AND CORRECT OUTPUTS, WHICH ALLOW THE MODEL
TO LEARN OVER TIME. THE ALGORITHM MEASURES ITS ACCURACY
THROUGH THE LOSS FUNCTION, ADJUSTING UNTIL THE ERROR HAS
BEEN SUFFICIENTLY MINIMIZED.
• SUPERVISED MACHINE LEARNING CAN BE SEPARATED INTO TWO TYPES
OF PROBLEMS WHEN DATA MINING - CLASSIFICATION AND
REGRESSION.

6. HOW SUPERVISED MACHINE LEARNING WORKS – CONT’D
TWO METHODS OF SUPERVISED MACHINE LEARNING - CLASSIFICATION AND REGRESSION.
• CLASSIFICATION – USES AN ALGORITHM TO ACCURATELY ASSIGN TEST
DATA INTO SPECIFIC CATEGORIES. IT RECOGNIZES SPECIFIC ENTITIES
WITHIN THE DATASET AND ATTEMPTS TO DRAW SOME CONCLUSIONS ON
HOW THESE ENTITIES SHOULD BE LABELLED OR DEFINED. COMMON
CLASSIFICATIONS ALGORITHMS ARE LINEAR CLASSIFIERS, SUPPORT
VECTOR MACHINES (SVM), DECISION TREES, K-NEAREST NEIGHBOR,
AND RANDOM FOREST.
• REGRESSION – IS USED TO UNDERSTAND THE RELATIONSHIP BETWEEN
DEPENDENT AND INDEPENDENT VARIABLES. IT IS COMMONLY USED TO
MAKE PROJECTIONS, SUCH AS SALES REVENUE FOR A GIVEN
BUSINESS. LINEAR REGRESSION, LOGISTICAL REGRESSION, AND
POLYNOMIAL REGRESSION ARE POPULAR REGRESSION ALGORITHMS.

7. FUTURE OF SUPERVISED MACHINE LEARNING –
CONT’D
• THE FUTURE OF SUPERVISED MACHINE LEARNING IS EXCEPTIONALLY EXCITING.
AT PRESENT, ALMOST EVERY COMMON DOMAIN IS POWERED BY MACHINE
LEARNING APPLICATIONS. TO NAME A FEW SUCH INDUSTRIES – HEALTHCARE,
SEARCH ENGINE, DIGITAL MARKETING, AND EDUCATION ARE THE MAJOR
BENEFICIARIES.
• SUPERVISED MACHINE LEARNING COULD BE CONTESTED MERIT TO AN
ENTERPRISE OR AN ORGANIZATION AS TASKS THAT ARE PRESENTLY BEING
DONE MANUALLY SHALL BE WHOLLY ACCOMPLISHED BY THE MACHINES IN THE
FUTURE. SUPERVISED MACHINE LEARNING BECOMES ARTIFICIAL
INTELLIGENCE’S GREATEST BLESSING TO HUMAN RACE FOR THE EFFECTIVE
REALIZATION OF THE TARGETS.

8. - CHAPTER 3 -
AREAS OF
APPLICATION

9. DIFFERENT APPLICATION OF SUPERVISED MACHINE
LEARNING
THERE ARE MANY APPLICATIONS ACROSS THE INDUSTRY, SINCE IT
PROVIDES THE BEST ALGORITHMS FOR FINDING ACCURATE RESULTS.
• FRAUD DETECTION IN BANKING AND FINANCE SECTOR.
• SPAM DETECTION.
• BIOINFORMATICS.
• OBJECT RECOGNITION.
• SPEECH RECOGNITION.

10. BENEFITS OF SUPERVISED MACHINE LEARNING
SUPERVISED MACHINE LEARNING IS OF GREAT BENEFIT TO THE SOCIETY IN
THE FOLLOWING WAYS:
• SUPERVISED LEARNING ALLOWS COLLECTING DATA AND PRODUCES DATA
OUTPUT FROM PREVIOUS EXPERIENCES.
• HELPS TO OPTIMIZE PERFORMANCE CRITERIA WITH THE HELP OF
EXPERIENCE.
• SUPERVISED MACHINE LEARNING HELPS TO SOLVE VARIOUS TYPES OF REAL-
WORLD COMPUTATION PROBLEMS.
• SUPERVISED MACHINE LEARNING HAS HIGHER ACCURACY AND IS
CONSIDERED TRUSTWORTHY, DUE TO HUMAN INVOLVEMENT.
• IT HELPS INDUSTRIES LIKE FINANCIAL INDUSTRIES, HEALTH SECTORS, SOCIAL
PREDICTORS AND DEMOGRAPHIC.
• IMPROVES CUSTOMER EXPERIENCE.
• IT REDUCES COST.

11. DOWNSIDES OF SUPERVISED MACHINE LEARNING
• CLASSIFYING BIG DATA CAN BE CHALLENGING.
• TRAINING FOR SUPERVISED LEARNING NEEDS A LOT OF COMPUTATION
TIME. SO, IT REQUIRES A LOT OF TIME.

12. THANK
YOU!