This document discusses mapping problems to AI approaches. It outlines the typical AI workflow of identifying a problem, preparing data, developing and training models, testing models, deploying models, and monitoring/optimizing. Key factors to consider are the problem to be predicted and available data. Common algorithms like linear regression, decision trees, KNN, and text classification are described along with the types of data they can handle. Basic data preparation steps are also outlined.

1. Mapping Problems to
AI
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2. This is our second meetup
• Stages of the mapping process
• Examples with Text Data and Numerical/Categorical Data
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3. The AI Workflow
• Identify problem
• Prepare data
• Develop models
• Train models
• Test models
• Deploy models
• Connect to app
• Monitor and optimize
• Repeat!
Data
Train
Model(s)
Develop
Model(s)
Test
Model(s)
Deploy
Model(s)
Connect
to
Business
app
Business
Need
Monitor
and
Optimize
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4. Factors to Consider about your Problem and
Your Data
• First – What is your problem?
• Can you describe your problem in the terms of what you want to predict and what
factors affect this prediction?
• Second – what data do you have?
• How is your data related to your problem?
Need to have at least some answer to these two questions before moving to
next stage
You CAN iterate, however, so your answers do NOT have to be perfect
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5. Your Mapping Process for the first three
lifecycle steps
What Type of Data do you have?, What Type of Prediction do you want?
Type of Data + Type of Prediction ==> AI approach
How to prepare your data for your AI approach?
What algorithm?
How to Tune? – NOT COVERED TODAY
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6. Mapping Problems to AI methods: A few
examples
What is the Data
What do we want to predict What do we want to predict
How to measure How to measure How to measure How to measure
How to tune
Numbers
Categories
Free form
Text
Category Number Category More text
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7. Example Workhorse Algorithms
Type of AI Example Algorithm What can it do? What types of Data
can it use?
Linear Regression Linear Leaner Predict numbers or
categories
Numerical, Categorical
Decision Trees XGBoost Predict numbers or
categories
Numerical, Categorical
K Nearest Neighbor KNN Predict numbers or
categories
Numerical, Categorical
Text Classification Bag of Words Predict Categories Text
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8. Your Mapping Process for the first three
lifecycle steps
What Type of Data do you have?, What Type of Prediction do you want?
Type of Data + Type of Prediction ==> AI approach
How to prepare your data for your AI approach?
What algorithm?
How to Tune? NOT COVERED TODAY
http://aiclub.world

9. Basic Data Preparation
• Missing Values
• Encoding Categories
• Finding irrelevant columns (or unparseable columns)
• Converting formats
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10. Your Mapping Process for the first three
lifecycle steps
What Type of Data do you have?, What Type of Prediction do you want?
Type of Data + Type of Prediction ==> AI approach
How to prepare your data for your AI approach?
What algorithm?
How to Tune? – NOT COVERED TODAY
http://aiclub.world

11. DemoDemo
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12. If you are interested in a free account, please
sign up at http://aiclub.world
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13. Thank you
Nisha@pyxeda.ai
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