Course curriculum
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1
Section 1: Basic Concepts
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Step 1: Artificial intelligence: What is it and where is it used?
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Step 2: Who can use AI?
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Step 3: Datasets
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Step 4: Predictive Model
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Step 5: Types of Problems
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Step 6: Workflow of a project with AI vs AI no-code (part 1)
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Step 7: Workflow of a project with AI vs AI no-code (part 2)
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2
Section 2: Setting Objectives and Preparing Data
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Step 1: Goals: First Problem
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Step 2: Goals: Second problem
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Step 3: Uploading the dataset
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Step 4: Exploring and changing columns
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Step 5: Overview
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Step 6: Distribution
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3
Section 3: Predicting the Continuous Variable
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Step 1: First steps
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Step 2: Analyzing the first result
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Step 3: Data Leakage
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Step 4: Prediction without data leakage
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Step 5: Recalculating
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Step 6: Filters
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First Challenge! Predicting the Price for Barcelona
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4
Section 4: Predicting the Categorical Variable
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Step 1: Neighborhood prediction
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Second Challenge! Neighborhood prediction without price or taxes
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5
Section 5: Analyzing the Results
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Step 1: Recapping models
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Step 2: Drivers
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Step 3: Correlation does not imply causality
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Step 4: Personas
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Step 5: Ideal persona
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Third Challenge: Creating a prototype of a persona
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6
Section 6: Exporting Predictions
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Step 1: Test results export
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Step 2: Exporting results to Web App
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Step 3: Exporting results to Google Drive
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Step 4: Predictions by batch
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7
Section 7: Advanced Analysis
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Step 1: Chosen model
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Step 2: Training, Validation, and Testing Metrics
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Step 3: Overfitting
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Step 4: Loss function
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Step 5: Advanced metrics
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Step 6: Regression Metrics
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Step 7: Classification Metrics
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Step 8: Advanced Analytics
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Step 9: Confusion matrix
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Step 10: Actual vs Predicted Value
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8
Section 8: Other Functionalities
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Step 1: Data store
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Step 2: Database connection
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9
Section 9: Conclusion
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Conclusion
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