- Level: Beginner
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to AI in Real Estate
Learning Outcome: Understand the basics of real estate price factors.1.1 Key factors affecting property prices.
1.2 Data types used in price prediction models.
1.3 Introduction to AI techniques for real estate.
Practical Component
Explore and visualize property datasets.
Week 2: Data Preprocessing
Learning Outcome: Learn how to clean and prepare real estate data.1.4 Handling missing values and outliers.
1.5 Feature engineering for real estate models.
1.6 Transforming data for AI models.
Practical Component
Prepare a dataset for price prediction.
Week 3: Building Price Prediction Models
Learning Outcome: Build basic regression models for predicting prices.1.7 Linear regression for price prediction.
1.8 Decision trees and random forests.
1.9 Comparing different models.
Practical Component
Build and evaluate a simple price prediction model.
Week 4: Improving Model Accuracy
Learning Outcome: Enhance model accuracy using advanced techniques.1.10 Cross-validation techniques.
1.11 Hyperparameter tuning.
1.12 Avoiding overfitting and underfitting.
Practical Component
Fine-tune a model for better accuracy.
Week 5: Real-World Challenges
Learning Outcome: Handle real-world complexities in price prediction.1.13 Data biases and ethical considerations.
1.14 Predicting prices in dynamic markets.
1.15 Real-time prediction systems.
Practical Component
Address a real-world data challenge.
Week 6: Final Project
Learning Outcome: Create a property price prediction system from scratch.1.16 Define a use case and gather data.
1.17 Build and test your AI solution.
1.18 Present your solution and receive feedback.
Practical Component
Demonstrate a working property price prediction tool.