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Machine Learning

I covered these topics in this repository:-

  1. Supervised Learning
    • Regression problems >> using models >> Linear Regression
    • Classification problems >> using models >> Logistic Regression
  2. Feature Engineering
    • Feature Scaling >> Normalization & Standardization(also known as z-score normalization)
    • Handling Categorical Data >> One-Hot Encoding & Ordinal Encoding & Label Encoding
    • Mathematical Transformations >> Function Transformers(log, square, squareroot, reciprocal transformers...) & Power Transformers(Box-Cox, Yeo-Johnson)
  3. Supervised Algorithms(Linear, Logistic, SVM, Naiye Bayes, Random Forest, Decision Tree Classifiers and Regressors)
  4. Unsupervised Algorithms(K-Means clustering)