Supervised learning is a type of machine learning focused on deriving predictions from labeled input-output pairs. It involves a process that includes historical data, training and test datasets, and validation to optimize model accuracy. The document also briefly mentions other machine learning types such as unsupervised learning and reinforcement learning.

1. Supervised Learning
Swipe

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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