This presentation is here to help you understand about Machine Learning, supervised Learning, Process Flow chat of Supervised Learning and 2 steps of supervised Learning.

1. Supervised Learning
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2. Machine learning is a branch of computer science that
focuses on the research and development of algorithms
that can learn from and predict data.
Rather of following purely static programme instructions,
such algorithms construct a model from sample inputs in
order to generate data-driven predictions or choices.
Machine Learning

3. Types of Machine Learning
Unsupervised
Reinforcement
Supervised

4. Supervised Learning is learning a function that maps an
input to an output based on data input-output pairs. It
infers a function from labeled training data consisting of a
set of training data
Types of Machine Learning
Unsupervised learning more specifically, clustering
include: Customer segmentation, or understanding
different customer groups. Unsupervised learning is
commonly used for finding meaningful patterns and
groupings inherent in a given or collected date set.
Reinforcement is a type of Machine Learning algorithm
which allows software agents and machines to
automatically determine the ideal behavior within a
specific context, to maximize its performance.

5. Supervised Learning Process Flow
.
Historical
data
.
Random
Sampling
.
Training
Dataset
.
Test
Dataset
.
Machine
Learning
.
Prediction,
Validation
.
Statistical
Model
.
Model
validation
outcome
Training and Validation

6. Supervised Learning Process Flow
.
New Data
.
Statistical
model
.
Prediction
outcome
Prediction
Prediction

7. Learning Training
Testing
Training
data
Learning
Algorithm
Supervised Process 2 Steps
Model
Test
data
Accuracy

8. Topics for next Post
Support Vector Machines
Linear regression
Logistic regression
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