Here are coursework of machine learning class based on Python and edited by Jupyter Notebooks. Topics covered in this course include:
- The KDD Process
- Using Python, Numpy, Pandas
- Using Scikit-learn modules
- Some emphasis on visualizing and understanding characteristics of the data
- Classification
- Regression Analysis
- Techniques such as KNN, Ridge Regression, Decision Tree and Bayesian classification
- Lots of emphasis on model evaluation
-Evaluation metrics
-Train-Test methodologies such as cross-validation
-Systematic parameter selection (e.g., grid search)
- Cluster analysis
- Using PCA and SVD for dimensionality reduction, data characterization, and noise reduction.
- Association rule discovery
- Emphasis on using unsupervised approaches as components of larger knowledge discovery efforts
-E.g., using PCA before clustering; using clustering as the basis for classification
- Text Mining and document analysis/filtering
- Recommender systems
- Predictive modeling for marketing/business applications
- Image analysis
- Markov models; time series analysis, sequential pattern mining
- Combining multiple classifiers
- Random Forest classifiers
- Other Meta-learners such as Ada Boost
- LDA Latent Dirichlet Allocation