Learned the fundamentals and applications in ML: Intro to Prob. & Linear algebra, Decision Theory, MLE & BE, Linear Model, Linear Discriminant function, Perceptron, FLD, PCA, Non-parametric Learning, Clustering, EM, GMM, EM and Latent Variable Model, Probabilistic Graphical Model, Bayesian Network, Neural Network, SVM, Decision Tree and Boosting
neural-network
clustering
linear-algebra
probability
bayesian-network
pca
perceptron
support-vector-machine
linear-model
gmm
decision-tree
probabilistic-graphical-models
latent-variable-models
decision-theory
boosting
maximum-likelihood-estimation
bayesian-estimation
linear-discriminant-function
non-parametric-learning
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Updated
Jan 20, 2024 - Jupyter Notebook