Supervised Machine Learning with Types and Techniques is for the mid level managers giving information about what is supervised machine learning, its types, how supervised machine learning, its advantages. You can also know the difference between Supervised and Unsupervised Machine learning to understand supervised machine learning in a better way for business growth. https://bit.ly/3ewivHm

1. Supervised Machine Learning with
Types and Techniques
Yo u r C o m p a n y N a m e

2. 2
Supervised Machine Learning01
o Types of Machine Learning
o What is Supervised Machine Learning?
o How Supervised Learning Works
o Types of Supervised Machine Learning Algorithms
o Supervised vs. Unsupervised Machine learning techniques
o Advantages of Supervised Learning:
o Disadvantages of Supervised Learning

3. Types of Machine Learning
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Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning
Inputs Outputs
Rewards
Inputs OutputsInputs Outputs
Training
Makes Machine Learn Explicitly
Data with Clearly defined Output is
given
Direct feedback is given
Predicts outcome/future
Resolves Classification and
Regression Problems
Machine Understands the data
(Identifies Patterns/ Structures)
Evaluation is Qualitative or Indirect
Does not Predict/Find anything
Specific
An approach to AI
Reward Based Learning
Learning form +ve & +ve
Reinforcement
Machine Learns how to act in a
Certain Environment
To Maximize Rewards

4. What is Supervised Machine Learning?
4
Input Raw Data
Processing
Output
Algorithm
Training
Data set
Desired
Output
Supervised Learning
Supervisor
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5. How Supervised Machine Learning works
5
Step 1
Provide the Machine Learning Algorithm Categorized or
“labeled” Input and Output Data from to Learn
Step 2
Feed the Machine New, Unlabeled Information to
See if it Tags New Data Appropriately. If not, Continue
Refining the Algorithm
Machine Machine
Group 1
Group 2
Types of Problems to which it’s Suited
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)
Label
“Group 1”
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6. Types of Supervised Machine Learning Algorithms
6
Classification
o Fraud Detection
o Email Spam Detection
o Diagnostics
o Image Classification
Regression
o Risk Assessment
o Score Prediction
Supervised
Learning
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7. Supervised vs. Unsupervised Machine Learning Techniques
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Supervised
Learning
o Classification
o Regression
Input & Output Data
Unsupervised
Learning
o Clustering
o Association
Input Data
Predictions &
Predictive Models
Patterns / Structure
Discovery

8. Advantages of Supervised Learning
8
It allows you to be very specific about the definition of the labels. In other words, you'll train
the algorithm to differentiate different classes where you'll set a perfect decision boundary.
You are ready to determine the amount of classes you would like to possess.
The input file is extremely documented and is labeled.
The results produced by the supervised method are more accurate and reliable as
compared to the results produced by the unsupervised techniques of machine learning. this
is often mainly because the input file within the supervised algorithm is documented and
labeled. this is often a key difference between supervised and unsupervised learning.
The answers within the analysis and therefore the output of your algorithm are likely to be
known thanks to that each one the classes used are known.
Advantages
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9. Disadvantages of Supervised Learning
9
o Supervised learning are often a
posh method as compared with
the unsupervised method. The
key reason is that you
simply need
to understand alright and label the
inputs in supervised learning.
o It doesn’t happen in real time while the
unsupervised learning is about the
important time. this is often also a
serious difference between supervised
and unsupervised learning. Supervised
machine learning uses of-line analysis.
o It is needed tons of computation
time for training.
o If you've got a dynamic big and
growing data, you're unsure of the
labels to predefine the principles.
this will be a true challenge.
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