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Data Science

Published

Sep 1, 2022

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Machine Learning Notes

#1: Components of a Learning System

Sep 2, 2021 06:34 PM

#2: Feasibility of Learning

Sep 7, 2021 06:02 PM

#3: Linear Regression, Regularization, Bias-Variance

Sep 9, 2021 06:36 PM

#4: Logistic Regression & Gradient Descent

Sep 14, 2021 04:39 PM

#5-6: Bayesian Machine Learning

Sep 21, 2021 05:49 PM

#7: SVM, Optimization

Oct 5, 2021 06:08 PM

Dual Form Solution, Kernel Trick, Hinge Loss

#8: Neural Networks

Oct 19, 2021 06:24 PM

XOR, Backpropagation Algorithm

#9: CNN & RNN

Oct 26, 2021 06:41 PM

+ LSTM

#10: Decision Trees

Nov 2, 2021 06:10 PM

+ Random Forest

#11: Ensemble Learning

Nov 9, 2021 07:18 PM

Bagging & Boosting

#12: Dimensionally Reduction

Nov 16, 2021 07:10 PM

Dimensionally Reduction, PCA, Matrix Factorization

#13-14: Clustering

Nov 30, 2021 07:14 PM

Hierarchical Clustering, Flat Clustering/KMeans, Gaussian Mixture Model, Expectation Maximization Algorithm

#15: Parametric Methods

Dec 9, 2021 06:25 PM

Density Estimation, KNN, Gaussian Process

#16: Reinforcement Learning

Dec 9, 2021 07:19 PM