alexyan21 / machine-learning-coursework

Machine learning coursework

Machine-Learning

Here are coursework of machine learning class based on Python and edited by Jupyter Notebooks. Topics covered in this course include:

Data Mining Overview

• The KDD Process

Data Preprocessing and Understanding

• Using Python, Numpy, Pandas
• Using Scikit-learn modules
• Some emphasis on visualizing and understanding characteristics of the data

Supervised Knowledge Discovery

• 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)
  

Unsupervised Knowledge Discovery

• 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

Real application domains

• Text Mining and document analysis/filtering
• Recommender systems
• Predictive modeling for marketing/business applications
• Image analysis

Approaches for mining sequential/temporal data

• Markov models; time series analysis, sequential pattern mining

More Ensemble and Hybrid Classifiers/Predictors

• Combining multiple classifiers
• Random Forest classifiers
• Other Meta-learners such as Ada Boost

Support Vector Machines and Kernel-Based Classifiers

Topic modeling with Latent factor models

• LDA Latent Dirichlet Allocation