1. machine-learning-lecture-notes
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jeonghunyoon's repositories
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03_classification.ipynb
Lecture01_Machine_Learning_Simple_Tutorial.ipynb
Lecture02_Linear_Regression.ipynb
Lecture03_Probability_Distribution.ipynb
Lecture04_Sub_Vectors-and-Matrices.ipynb
Lecture05_Numpy.ipynb
Lecture05_Sub_KNN_Using_Numpy.ipynb
Lecture05_Sub_Numpy_Problem01.ipynb
Lecture06_Hypothesis_Test.ipynb
Lecture06_Sub_LSA.ipynb
Lecture06_Sub_LSA_2.ipynb
Lecture07_Pandas_1.ipynb
Lecture07_Pandas_2.ipynb
Lecture07_Sub_Pandas_Analysis_Examples.ipynb
Lecture07_Sub_Pandas_Problem01.ipynb
Lecture08_Matplotlib_1.ipynb
Lecture08_Matplotlib_2.ipynb
Lecture09_Sub_Gradient_01.ipynb
Lecture09_Sub_Gradient_02.ipynb
Lecture10_EDA.ipynb
Lecture10_Statistics_In_Data_Science_1.ipynb
Lecture10_Statistics_In_Data_Science_2.ipynb
Lecture11_Gaussian_Naive_Bayes.ipynb
Lecture11_Sentiment_Classifier_Using_Naive_Bayes_From_The_Scratch.ipynb
Lecture11_Sentiment_Classifier_Using_Naive_Bayes_With_SKlearn.ipynb
Lecture13_Linear_Regression_ML_Model.ipynb
Lecture13_Linear_Regression_Stat_Model.ipynb
Lecture14_Binary_Classification_MNIST.ipynb
Lecture14_Multiclass_Classification_MNIST.ipynb
Lecture15_Logistic_Regression_ML.ipynb
Lecture15_Logistic_Regression_Stat.ipynb
Lecture16_Decision_Tree_Python_1.ipynb
Lecture16_Decision_Tree_Python_2.ipynb
Lecture17_Creating_Random_Forest.ipynb
Lecture17_Ensemble_1.ipynb
Lecture17_Ensemble_2.ipynb
Lecture17_Stacking.ipynb
Lecture18_SVM.ipynb
Lecture19_Dimesional_Reduction_With_PCA.ipynb
Lecture19_PCA.ipynb
Lecture19_PCA_By_Hands.ipynb
Lecture20_Customer_Segmentation_Easy_version.ipynb
Lecture20_Customer_Segmentation_Full_Version.ipynb
Lecture20_K_Means_Clustering.ipynb
Lecture21_Clustering.ipynb
Lecture22_CardFraudDetection.ipynb
Lecture22_DNN.ipynb
Lecture24_Time_Series_01.ipynb
Lecture24_Time_Series_02.ipynb
utilizing-arima-to-forecast-uber-s-market-demand.ipynb
Lecture02_Probabilities.pdf
Lecture03_Probability_Distribution_01.pdf
Lecture03_Probability_Distribution_02.pdf
Lecture04_Linear_Algebra_Basic_Matrix.pdf
Lecture04_Linear_Algebra_Basic_Vector.pdf
Lecture06_Spectral_Theorem_Eigenvalue.pdf
Lecture06_Spectral_Theorem_Transformation.pdf
Lecture09_Gradient.pdf
Lecture10_Anova.pdf
Lecture10_Correlation.pdf
Lecture10_Estimation_Theory.pdf
Lecture10_Hypothesis_Testing_01.pdf
Lecture10_Hypothesis_Testing_02.pdf
Lecture10_Sample_Distribution.pdf
Lecture11_Basic_Concept_of_Machine_Learning.pdf
Lecture12_Bayesian_Decision_Thoery.pdf
Lecture12_MLE_MAP.pdf
Lecture13_Linear_Regression.pdf
Lecture15_Logistic_Regression.pdf
Lecture16_Decision_Tree_Information_Theory.pdf
Lecture17_Ensemble_Models_1.pdf
Lecture17_Ensemble_Models_2.pdf
Lecture18_Convex_optimization.pdf
Lecture18_SVM.pdf
Lecture19_PCA.pdf
Lecture20_K_means_clustering.pdf
Lecture22_DNN.pdf
Lecture23_Feature_Selection.pdf
Lecture25_Association_Rule_Mining.pdf
Lecture26_Topic_models.pdf
README.md