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Course Outline
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Machine Learning with Python
- Choice of libraries
- Add-on tools
Regression
- Linear regression
- Generalizations and Nonlinearity
- Exercises
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercises
Cross-validation and Resampling
- Cross-validation approaches
- Bootstrap
- Exercises
Unsupervised Learning
- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means
Requirements
Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.
Testimonials
All like it
蒙 李
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