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Machine Learning - Coggle Diagram
Machine Learning
ML Provides statistical
tools to analyze,
visualize, and make
predictions from data
Eg. Online shoping
Netflix movie
suggestions
Spm mail
warning
Self-driving cars
Types of
Machine
Learning
Supervised Learning
Classify data
or make
predictions
-Disease detection
-Weather forecasting
-stock price prediction
-spam detection
-credit scoring
Based on output label
Output label categorical
Classification
Output label continuous
Regression
Unsupervised
Understand relationships
within datasets
Fraudulent transactions detection
Customer segmentation
Outlier detection
Targeted marketing campaigns
There no labeled outputs
Algorithms learn the patterns in data
and group similar items
Clustering is the
grouping similar
data items
Data items are
more similar within a
cluster than items
outside it.
Unsupervised Workflow
Prepare data
Create similarity metrics
Run clustering algorithm
Interpret results and adjust clustering
Reinforcement
Make decisions
or Choices
Automated robots
Autonomous cars
Video games
Healthcare
Enable the agent to
learn from its
interaction
with env
Receives feedback
rewards
penalities
without any labeled dataset
AGENT
ENVIRONMENT
STATE
ACTION
POLICY
ML PROCESS
-Loding data
-Preproc
-Train a model
-Evaluate the model
-Making predictions
Demo
Demo2
Input Features
and Output Labels
example
Training
dataset
Trained
Model
Inference
Applying this
capability to new
data
ANACONDA
Anacounda App
Jupiter Notebook
A subset of IA
focused on systems
that can learn and
improve from experience
Powered by Algorithms
that incorporate intelligence
into machines
