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..
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ai-and-ml
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bagging-boosting-rf
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choosing-technique
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classifier-history
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classifier_categories
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clf-behavior-data
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closed-form-vs-gd
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datascience-ml
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decision-tree-binary
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decisiontree-error-vs-entropy
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diff-perceptron-adaline-neuralnet
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difference-deep-and-normal-learning
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dimensionality-reduction
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euclidean-distance
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evaluate-a-model
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issues-with-clustering
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large-num-features
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lda-vs-pca
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linear-gradient-derivative
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logistic-why-sigmoid
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logistic_regression_linear
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logisticregr-neuralnet
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median-vs-mean
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ml-curriculum
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ml-examples
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ml-solvable
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multiclass-metric
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naive-bayes-boundary
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naive-bayes-vartypes
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naive-naive-bayes
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neuralnet-error
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overfitting
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pca-scaling
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pearson-r-vs-linear-regr
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probablistic-logistic-regression
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regularized-logistic-regression-performance
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select_svm_kernels
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softmax
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softmax_regression
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svm_for_categorical_data
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tensorflow-vs-scikitlearn
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visual-backpropagation
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why-python
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README.md
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ai-and-ml.md
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avoid-overfitting.md
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bag-of-words-sparsity.md
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bagging-boosting-rf.md
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best-ml-algo.md
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choosing-technique.md
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classifier-categories.md
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classifier-history.md
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clf-behavior-data.md
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closed-form-vs-gd.md
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computing-the-f1-score.md
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copyright.md
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cost-vs-loss.md
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data-science-career.md
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datamining-overview.md
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datamining-vs-ml.md
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dataprep-vs-dataengin.md
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datascience-ml.md
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decision-tree-binary.md
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decision-tree-disadvantages.md
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decisiontree-error-vs-entropy.md
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deep-learning-resources.md
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deeplearn-vs-svm-randomforest.md
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deeplearning-criticism.md
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definition_data-science.md
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diff-perceptron-adaline-neuralnet.md
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difference-deep-and-normal-learning.md
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difference_classifier_model.md
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different.md
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dimensionality-reduction.md
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dropout.md
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euclidean-distance.md
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evaluate-a-model.md
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feature_sele_categories.md
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implementing-from-scratch.md
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inventing-deeplearning.md
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issues-with-clustering.md
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large-num-features.md
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lazy-knn.md
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lda-vs-pca.md
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linear-gradient-derivative.md
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logistic-analytical.md
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logistic-boosting.md
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logistic-why-sigmoid.md
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logistic_regression_linear.md
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logisticregr-neuralnet.md
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many-deeplearning-libs.md
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median-vs-mean.md
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mentor.md
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missing-data.md
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ml-curriculum.md
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ml-examples.md
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ml-origins.md
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ml-python-communities.md
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ml-solvable.md
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ml-to-a-programmer.md
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model-selection-in-datascience.md
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multiclass-metric.md
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naive-bayes-boundary.md
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naive-bayes-vartypes.md
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naive-bayes-vs-logistic-regression.md
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naive-naive-bayes.md
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neuralnet-error.md
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nnet-debugging-checklist.md
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num-support-vectors.md
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number-of-kfolds.md
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open-source.md
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overfitting.md
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parametric_vs_nonparametric.md
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pca-scaling.md
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pearson-r-vs-linear-regr.md
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prerequisites.md
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probablistic-logistic-regression.md
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py2py3.md
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r-in-datascience.md
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random-forest-perform-terribly.md
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regularized-logistic-regression-performance.md
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return_self_idiom.md
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scale-training-test.md
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select_svm_kernels.md
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semi-vs-supervised.md
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softmax.md
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softmax_regression.md
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standardize-param-reuse.md
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svm_for_categorical_data.md
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technologies.md
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tensorflow-vs-scikitlearn.md
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underscore-convention.md
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version.md
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visual-backpropagation.md
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when-to-standardize.md
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why-python.md
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