Machine-Learning-Notebooks

These notebooks contain small projects and notes.

Contents

Basic Machine Learning Models

• Linear Regression
• Gradient Descent
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Mini-batch Gradient Descent
• Regularized Linear Models
  • Polynomial Regression
  • Ridge Regression
  • Lasso Regression
  • Elastic Net
• Logistic Regression
• Softmax Regression

Support Vector Machines (SVMs)

• Linear SVM Classification
• Nonlinear SVM Classification

Decision Trees

• Classification Example using ScikitLearn
• The Classification and Regression Tree (CART) Algorithm
• Regression Example using ScikitLearn

Ensemble Methods

• Why ensemble methods can work better than individual classifiers alone
• Voting Classifiers
  • Hard Voting (majority vote)
  • Soft Voting (weighted majority vote based on class probabilities)
• Bagging and Pasting
• Random Forests
  • Extremely Randomized Trees
  • Feature Importance
• Boosting
  • AdaBoost
  • Gradient Boosting
    • Gradient Tree Boosting
    • Stochastic gradient boosting
    • Histogram-Based Gradient Boosting
• Stacking (or stacked generalization)

References

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurélien Géron.

• Python Data Science Handbook - Second Edition, by Jake VanderPlas

• Machine Learning with PyTorch and Scikit-Learn, by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili

• Python Machine Learning By Example - Third Edition, by Yuxi (Hayden) Liu

• Angela Yu's 100 Days of Code: The Complete Python Pro Bootcamp