├── Deep learning in Python ├── 01-basics-of-deep-learning-and-neural-networks │ ├── 01-coding-the-forward-propagation-algorithm.py │ ├── 02-the-rectified-linear-activation-function.py │ ├── 03-applying-the-network-to-many-observations-rows-of-data.py │ ├── 04-multi-layer-neural-network.py │ ├── ch1_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 02-optimizing-a-neural-network-with-backward-propagation │ ├── 01-coding-how-weight-changes-affect-accuracy.py │ ├── 02-scaling-up-to-multiple-data-points.py │ ├── 03-calculating-slopes.py │ ├── 04-improving-model-weights.py │ ├── 05-making-multiple-updates-to-weights.py │ ├── ch2_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 03-building-deep-learning-models-with-keras │ ├── 01-specifying-a-model.py │ ├── 02-compiling-the-model.py │ ├── 03-fitting-the-model.py │ ├── 04-last-steps-in-classification-models.py │ ├── 05-making-predictions.py │ ├── ch3_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 04-fine-tuning-keras-models │ ├── 01-changing-optimization-parameters.py │ ├── 02-evaluating-model-accuracy-on-validation-dataset.py │ ├── 03-early-stopping-optimizing-the-optimization.py │ ├── 04-experimenting-with-wider-networks.py │ ├── 05-adding-laters-to-a-network.py │ ├── 06-building-your-own-digit-recognition-model.py │ ├── ch4_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── course-description.rtf └── data │ ├── hourly_wages.csv │ ├── mnist.csv │ └── titanic_all_numeric.csv ├── Linear Classifiers in Python ├── 1.Applying logistic regression and SVM │ ├── KNN classification.py │ ├── Running LogisticRegression and SVC.py │ ├── Sentiment analysis for movie reviews.py │ ├── Visualizing decision boundaries.py │ └── chapter1.pdf ├── 2.Loss functions │ ├── Changing the model coefficients.py │ ├── Comparing the logistic and hinge losses.py │ ├── Implementing logistic regression.py │ ├── Minimizing a loss function.py │ └── chapter2.pdf ├── 3.Logistic regression │ ├── Fitting multi-class logistic regression.py │ ├── Identifying the most positive and negative words.py │ ├── Logistic regression and feature selection.py │ ├── One-vs-rest SVM.py │ ├── Regularization and probabilities.py │ ├── Regularized logistic regression.py │ ├── Visualizing easy and difficult examples.py │ ├── Visualizing multi-class logistic regression.py │ └── chapter3.pdf └── 4.Support Vector Machines │ ├── Effect of removing examples.py │ ├── GridSearchCV warm-up.py │ ├── Jointly tuning gamma and C with GridSearchCV.py │ ├── Using SGDClassifier.py │ └── chapter4.pdf ├── README.md ├── Supervised learning with scikit-learn ├── 01-classification │ ├── 01-k-nearest-neighbors-fit.py │ ├── 02-k-nearest-neighbors-predict.py │ ├── 03-the-digits-recognition-dataset.py │ ├── 04-train-test-split-fit-predict-accuracy.py │ ├── 05-overfitting-and-underfitting.py │ ├── ch1_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 02-regression │ ├── 01-importing-data-for-supervised-learning.py │ ├── 02-fit-and-predict-for-regression.py │ ├── 03-train-test-split-for-regression.py │ ├── 04-5-fold-cross-validation.py │ ├── 05-k-fold-cv-comparison.py │ ├── 06-regularization-I-lasso.py │ ├── 07-regularization-ii-ridge.py │ ├── ch2_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 03-fine-tuning-your-model │ ├── 01-metrics-for-classification.py │ ├── 02-building-a-logistic-regression-model.py │ ├── 03-plotting-an-roc-curve.py │ ├── 04-hyperparameter-tuning-with-gridsearchcv.py │ ├── 05-hyperparameter-tuning-with-randomizedsearchcv.py │ ├── 06-hold-out-set-in-practice-i-classification.py │ ├── 07-hold-out-set-in-practice-ii-regression.py │ ├── ch3_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 04-preprocessing-pipelines │ ├── 01-exploring-categorical-features.py │ ├── 02-creating-dummy-variables.py │ ├── 03-regression-with-categorical-features.py │ ├── 04-dropped-missing-data.py │ ├── 05-imputing-missing-data-in-a-ml-pipeline-i.py │ ├── 06-imputing-missing-data-in-a-ml-pipeline-ii.py │ ├── 07-centering-and-scaling-your-data.py │ ├── 08-centering-and-scaling-in-a-pipeline.py │ ├── 09-bringing-it-all-together-1-pipeline-for-classification.py │ ├── 10-bringing-it-all-together-2-pipeline-for-regression.py │ ├── ch4_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── course-description.png ├── course-description.rtf └── data │ ├── auto.csv │ ├── boston.csv │ ├── diabetes.csv │ ├── gm_2008_region.csv │ ├── house-votes-84.csv │ ├── white-wine.csv │ └── winequality-red.csv ├── Unsupervised learning in python ├── 01-clustering-for-dataset-exploration │ ├── 01-how-many-clusters.py │ ├── 02-clustering-2d-points.py │ ├── 03-inspect-your-clustering.py │ ├── 04-how-many-clusters-of-grain.py │ ├── 05-evaluating-the-grain-clustering.py │ ├── 06-scaling-fish-data-for-clustering.py │ ├── 07-clustering-the-fish-data.py │ ├── 08-clustering-stocks-using-kmeans.py │ ├── 09-which-stocks-move-together.py │ ├── ch1_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 02-visualization-with-hierarchical-clustering-and-t-sne │ ├── 01-hierarchical-clustering-of-the-grain-data.py │ ├── 02-hierarchies-of-stocks.py │ ├── 03-different-linkage-different-hierarchical-clustering.py │ ├── 04-extracting-the-cluster-labels.py │ ├── 05-tsne-visualization-of-grain-dataset.py │ ├── 06-a-tsne-map-of-the-stock-market.py │ ├── ch2_slides.pdf │ ├── chapter-details.png │ └── chapter-details.rtf ├── 03-decorrelating-your-data-and-dimension-reduction │ ├── 01-correlated-data-in-nature.py │ ├── 02-decorrelating-the-grain-measurements-with-pca.py │ ├── 03-the-first-principal-component.py │ ├── 04-variance-of-the-pca-features.py │ ├── 05-dimension-reduction-of-the-fish-measuremenys.py │ ├── 06-a-tfidf-word-frequency-array.py │ ├── 07-clustering-wikipedia-part-1.py │ ├── 08-clustering-wikipedia-part-2.py │ ├── ch3_slides.pdf │ ├── chapter-details.png │ └── 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