An implementation of some classical machine learning algorithms

This is a Python library implementing some classical machine learning algorithms from scratch using numpy, which was done in Machine Learning Course at HCMUT.

List of algorithms:

Linear Regression
Logistic Regression
Softmax Regression
Support Vector Machine (SVM)
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
ID3 Decision Tree
Hidden Markov Model (HMM)

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
pylearn_ml191		pylearn_ml191
README.md		README.md
decision_tree.ipynb		decision_tree.ipynb
generator.ipynb		generator.ipynb
hmm-final-exam.ipynb		hmm-final-exam.ipynb
hmm.ipynb		hmm.ipynb
kernel-svm.ipynb		kernel-svm.ipynb
lda.ipynb		lda.ipynb
linear_regression.ipynb		linear_regression.ipynb
logistic_regression.ipynb		logistic_regression.ipynb
midterm_softmax.ipynb		midterm_softmax.ipynb
midterm_svm.ipynb		midterm_svm.ipynb
mnist.ipynb		mnist.ipynb
mnist_feature_extraction_pytorch.ipynb		mnist_feature_extraction_pytorch.ipynb
pca.ipynb		pca.ipynb
primal_svm.ipynb		primal_svm.ipynb
softmax_regression.ipynb		softmax_regression.ipynb
weather.csv		weather.csv

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An implementation of some classical machine learning algorithms

List of algorithms:

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