ML-from-scratch

Tried to implement some common machine learning algorithms from scratch.

What it includes?

• Logistic regression
  • Sigmoid Function
  • Stochastic Gradient Descent
  • AdaGrad
  • RMSProp
• Feed forward Neural Network
  • Loss Function
  • Gradient Descent
  • Back Propagation
  • Regularization
  • Plot of decision boundary
• Principal Component Analysis
  • Eigen Value Decomposition
  • Kaiser Rule for selecting components.
  • Scree Plot rule for selecting components.
• K Nearest Neighbors
  • Similarity Function
  • Regular KNN
  • Weighted KNN
• Decision Tree
  • Gini Index Function
  • Entropy Function
  • CART algorithm
• KMeans algorithm
  • Random assignment.
  • Initializing using KMeans++.
• Perceptron Learning Algorithm (PLA)