This repository contains essential ML topics and coding implementations. I'm keeping updating...

• Linear Regression: Gradient descent, normal equation, Lasso/Ridge regularization.
• Logistic Regression: Binary/multi-class classification, cross-entropy loss, gradient computation.
• SVM: Kernel functions, soft/hard margin, maximum margin classifier.
• Decision Tree & Random Forest: Information gain, Gini index, pruning, feature importance.
• KNN: Euclidean distance, K-value selection, KD-tree optimization.
• K-Means Clustering: Elbow method, KMeans++ initialization.
• Naive Bayes: Probability computation, Laplace smoothing.

• DNN: Forward and backward propagation, matrix operations.
• CNN: Convolution, pooling, batch normalization.
• RNN, LSTM, GRU: Time series modeling, vanishing/exploding gradients.
• Transformer & Self-Attention (for NLP roles).
• Autoencoder & VAE: Dimensionality reduction, generative modeling.