The portfolio of projects in this repository cover the following:
- A1: Exploring and visualizing data
- A2: Evaluating regression models
- A3: Evaluating classification models
- A4: Random forests and gradient boosting
- A5: Principal Components Analysis (PCA)
- A6: Neural networks
- A7: Image processing with CNNs
- A8: Language modeling with RNNs
Formal reports on each project - outlining data source, objectives, methods, results, and conclusions - are contained in the pdfs folder.
Course overview
The course serves as an introduction to machine learning with business applications. It provides a survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification.
Learning outcomes include:
- Describe machine learning applications in business
- Compare traditional statistical methods and machine learning methods
- Distinguish between supervised and unsupervised learning methods
- Design studies for training and testing machine learning methods
- Evaluate machine learning models for regression and classification
- Construct trees, random forests, gradient boosted models, and neural networks
- Explore deep learning models for vision and natural language processing