MISY331: Machine Learning for Business
“Talk is cheap. Show me the code.” - Linus Torvalds
This course introduces the basic concepts and techniques of machine learning and covers most commonly used models for predictive analytics. The end-to-end workflow for typical machine learning projects is illustrated via multiple business programming cases and Kaggle competitions. If time permits, deep learning techniques are also introduced. This course is programming intensive using Python 3 and popular packages, such as Jupyter, Numbpy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.
Note: this course was named MISY467 when first taught in the 2020 Spring semester.
- Machine Learning Overview
- Toolkit Bootcamp (git, python, jupyter, numpy, pandas, matplotlib, seanborn, scikit-Learn)
- Exploratory Data Analysis (EDA)
- Data Preprocessing (missing data, outliers, feature encoding, pipeline, etc.)
- Model Training, Evaluation, and Tuning
- Classification (Decision Tree, Logistic Regression)
- Regression (Linear Regression, Gradient Descent, SVM)
- Ensemble Learning (Random Forest, Gradient Boosting)
- Clustering (K-Means)
- Dimensionality Reduction
Professor Harry J. Wang: check out my website at harrywang.me
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