Master Machine Learning Algorithms 📕

This is to practice the machine learning algorithms mentioned in the book Master Machine Learning Algorithms by Jason Brownlee. I'll first manually implement the algorithms using a generated sample data based on the tutorials in the book and excel sheets provided by the book, then apply the related function from sklearn library, and finally apply that function to a previously collected dataset. The organization of this repository as follows:

• Linear Algorithms
  • Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis (LDA)
• Nonlinear Algorithms
  • Classification and Regression Trees (CART)
  • Naive Bayes
  • Gaussian Naive Bayes (GNB)
  • $k$-Nearest Neighbors (kNN)
  • Learning Vector Quantization (LVQ)
  • Support Vector Machines (SVM)
• Ensemble Algorithms
  • Bagging and Random Forest
  • Boosting and AdaBoost

These algorithms can be group in a different way as well. Linear Regression, Logistic Regression, and SVMs are methods where the form of the model was pre-defined. On the other hand, $k$-Nearest Neighbours and Decision Trees are non-parametric learners which do not have a model structure specified a priori [1].

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[1] Machine Learning for Humans