Executive Development Programme in Model Development: Essentials

• Introduction to Model Development: Overview of model development, its importance, and applications.
• Data Preprocessing: Data cleaning, normalization, and transformation techniques.
• Feature Engineering: Techniques to extract, transform, and select features to improve model performance.
• Regression Models: Linear and logistic regression, least squares method, and regularization techniques.
• Classification Models: Decision trees, random forests, support vector machines, and naive Bayes.
• Model Evaluation: Performance metrics, cross-validation, and statistical tests.
• Supervised Learning: Overview of supervised learning, algorithms, and applications.
• Unsupervised Learning: Overview of unsupervised learning, clustering, and dimensionality reduction.
• Deep Learning: Neural networks, backpropagation, and convolutional neural networks.
• Model Deployment: Model deployment strategies, version control, and monitoring techniques.