TITLE: Training on Machine Learning and Artificial Neural Networks (ANNs) in Real Estate Price Prediction
Course Objectives:
By the end of this training, participants will be able to:
- To introduce participants to machine learning and artificial neural networks (ANNs) in the context of real estate price prediction.
- To provide a foundational understanding of key mathematical concepts underlying machine learning and optimization techniques.
- To explore the practical applications of ML and ANNs in predicting real estate prices.
- To enable participants to evaluate and compare different predictive models.
Target Audience:
- Real estate professionals interested in data-driven decision making.
- Data scientists and analysts focusing on real estate markets.
- Graduate students and researchers in real estate economics and related fields.
- Professionals with a basic understanding of statistics and linear algebra.
Learning Outcomes:
- Understand the basic principles and methodologies of machine learning and ANNs.
- Apply gradient descent and optimization techniques to improve model performance.
- Analyze and preprocess real estate data for predictive modeling.
- Develop and evaluate regression and ANN models for real estate price prediction.
- Understand the theoretical underpinnings of model training and optimization.
The agendas are subject to change. All registered participants will receive the final version of the agenda via email.