├── .gitignore ├── 01-Python-Crash-Course ├── 01-Python Crash Course.ipynb ├── 02-Python Crash Course Exercises.ipynb └── 03-Python Crash Course Exercises - Solutions.ipynb ├── 02-Numpy ├── 00-NumPy-Arrays.ipynb ├── 01-NumPy-Indexing-and-Selection.ipynb ├── 02-NumPy-Operations.ipynb ├── 03-NumPy-Exercises.ipynb ├── 04-NumPy-Exercises-Solutions.ipynb ├── NumPy Basics.ipynb ├── NumPy Indexing and Selection.ipynb ├── NumPy Operations.ipynb ├── Untitled.ipynb ├── Untitled1.ipynb ├── Untitled2.ipynb ├── axis_logic.png └── numpy_indexing.png ├── 03-Pandas ├── 00-Series.ipynb ├── 01-DataFrames.ipynb ├── 02-Conditional-Filtering.ipynb ├── 03-Useful-Methods.ipynb ├── 04-Missing-Data.ipynb ├── 05-Groupby-Operations-and-MultiIndex.ipynb ├── 06-Combining-DataFrames.ipynb ├── 07-Text-Methods.ipynb ├── 08-Time-Methods.ipynb ├── 09-Inputs-and-Outputs.ipynb ├── 10-Pivot-Tables.ipynb ├── 11-Pandas-Project-Exercise .ipynb ├── 12-Pandas-Project-Exercise-Solution.ipynb ├── Concatenation.ipynb ├── Conditional Filtering.ipynb ├── Data Input and Output.ipynb ├── DataFrames.ipynb ├── Date and Time Methods.ipynb ├── DropBy Operations.ipynb ├── Merging.ipynb ├── Missing Data.ipynb ├── Pandas Basics.ipynb ├── Pivot Tables.ipynb ├── Text Methods.ipynb ├── Untitled.ipynb ├── Useful Methods.ipynb ├── __pycache__ │ └── pydbgen.cpython-36.pyc ├── example.xlsx ├── my_excel_file.xlsx ├── new_workbook.xlsx ├── newexcelfile.xlsx ├── reshaping_pivot.png ├── sample_table.html └── simple.html ├── 04-Matplotlib ├── 00-Matplotlib-Basics.ipynb ├── 01-Matplotlib-Figures.ipynb ├── 02-Matplotlib-SubPlots.ipynb ├── 03-Matplotlib-Styling-Plots.ipynb ├── 04-Matplotlib Exercises .ipynb ├── 05-Matplotlib Exercises - Solutions.ipynb ├── 06-Additional-Matplotlib-Commands-NO_VIDEO.ipynb ├── Matplotlib Basics.ipynb ├── Matplotlib Styling.ipynb ├── Matplotlib Subplots.ipynb ├── Untitled.ipynb ├── example.png ├── figure.png ├── myfirstplot.png ├── new_figure.png ├── new_subplots.png ├── subplots.png └── test.png ├── 05-Seaborn ├── 00-Scatter-Plots.ipynb ├── 01-Distribution-Plots.ipynb ├── 02-Categorical-Plots-Stat-Estimation.ipynb ├── 03-Categorical-Plots-Distributions.ipynb ├── 04-Comparison-Plots.ipynb ├── 05-Seaborn-Grids.ipynb ├── 06-Matrix-Plots.ipynb ├── 07-Seaborn-Exercise.ipynb ├── 08-Seaborn-Exercise-Solutions.ipynb ├── Categorical Plots.ipynb ├── Comparison Plots.ipynb ├── DistPlot_solution.png ├── Distribution Plots.ipynb ├── Distribution within Categories.ipynb ├── Matrix Plots.ipynb ├── Seaborn Basics.ipynb ├── Seaborn Grids.ipynb ├── Seabron Basics.ipynb ├── catplot_solution.png ├── example_scatter.jpg ├── heatmap_solution.png ├── my_plot.jpg └── task_one.jpg ├── 06-Capstone-Project ├── 00-Capstone-Project.ipynb └── 01-Capstone-Project-Solutions .ipynb ├── 07-Overview-of-Machine-Learning ├── ML Basics.ipynb └── Template.ipynb ├── 08-Linear-Regression-Models ├── 00-Intro-to-Simple-Linear-Regression.ipynb ├── 01-Linear-Regression-with-Scitkit-Learn.ipynb ├── 02-Polynomial-Regression.ipynb ├── 03-Regularization-Ridge-Lasso-ElasticNet.ipynb ├── 04-Linear-Regression-Project-DataSet .ipynb ├── Bias-Variance Trade Off.ipynb ├── Cross-Validation.ipynb ├── Elastic Net.ipynb ├── Linear Regression Basics.ipynb ├── Polynomial Regression.ipynb ├── Regularisation for Linear Regression.ipynb ├── Regularisation.ipynb ├── Scikit-Learn Basics.ipynb ├── Simple Linear Regression.ipynb ├── final_converter.joblib ├── final_poly_converter.joblib ├── final_poly_model.joblib ├── final_sales_model.joblib ├── poly_converter.joblib ├── sales_model.joblib └── sales_poly_model.joblib ├── 09-Feature-Engineering ├── 00-Dealing-with-Outliers.ipynb ├── 01-Dealing-with-Missing-Data.ipynb ├── 02-Dealing-with-Categorical-Data.ipynb ├── Categorical Feature Encoding.ipynb └── Feature Engineering Basics.ipynb ├── 10-Cross-Val-and-LinReg-Project ├── 00-Cross-Validation.ipynb ├── 01-Grid-Search.ipynb ├── 02-Linear-Regression-Project-Exercise.ipynb ├── 03-Linear-Regression-Project-Exercise-Solutions.ipynb ├── Cross Validation.ipynb └── grid_search_cross_validation.png ├── 11-Logistic-Regression-Models ├── 00-Logistic-Regression.ipynb ├── 01-Multi-Class-Logistic-Regression.ipynb ├── 02-Logistic-Regression-Project-Exercise .ipynb ├── 03-Logistic-Regression-Project-Exercise-Solution.ipynb └── Logistic Regression Basics.ipynb ├── 12-K-Nearest-Neighbors ├── 00-KNN-Classification.ipynb ├── 01-KNN-Exercise .ipynb ├── 02-KNN-Exercise-Solutions.ipynb ├── KNN Basics.ipynb ├── mine.jpg └── sonar.jpg ├── 13-Support-Vector-Machines ├── 00-SVM-Classification.ipynb ├── 01-SVM-Regression.ipynb ├── 02-SVM-Project-Exercise.ipynb ├── 03-SVM-Project-Exercise-Solutions.ipynb ├── SVM with regression.ipynb ├── Support Vector Machine Basics.ipynb ├── Types_of_concrete_slump.jpg ├── __pycache__ │ ├── svm_margin_plot.cpython-37.pyc │ └── svm_margin_plot.cpython-38.pyc ├── svm_margin_plot.py └── wine.jpg ├── 14-Decision-Trees ├── 00-Decision-Trees.ipynb ├── Decision Tree Basics.ipynb └── penguin.jpg ├── 15-Random-Forests ├── 00-Random-Forest-Classification.ipynb ├── 01-Random-Forest-Regression.ipynb ├── Random Forest Basics.ipynb ├── Random Forest Regression.ipynb ├── boring.jpg └── penguin.jpg ├── 16-Boosted-Trees ├── 00-AdaBoost.ipynb ├── 01-Gradient-Boosting.ipynb ├── Boosted Trees Basics.ipynb └── mushroom.jpg ├── 17-Supervised-Learning-Capstone-Project ├── 00-Sup-Learning-Capstone-Tree-Methods .ipynb ├── 01-Sup-Learning-Capstone-Tree-Methods-SOLNs.ipynb ├── cplot.png ├── fig1.png ├── fig10.png ├── fig11.png ├── fig2.png ├── fig3.png ├── fig5.png ├── fig6.png ├── fig7.png ├── fig9.png ├── figbar.png └── hugetree.png ├── 18-Naive-Bayes-and-NLP ├── 00-Feature-Extraction-From-Text.ipynb ├── 01-Text-Classification.ipynb ├── 02-Text-Classification-Assessment .ipynb ├── 03-Text-Classification-Assessment-Solution.ipynb ├── Classification of Text.ipynb ├── Naive Bayes and NLP Basics.ipynb ├── One.txt └── Two.txt ├── 19-Unsupervised-Learning-Overview ├── Basics.ipynb └── Blank on Purpose.ipynb ├── 20-Kmeans-Clustering ├── 00-Kmeans-Clustering.ipynb ├── 01-Kmeans-Color-Quantization.ipynb ├── 02-Kmeans-Clustering-Project-Exercise.ipynb ├── 03-Kmeans-Clustering-Project-Solutions.ipynb ├── Basics.ipynb └── Colour Quantisation.ipynb ├── 21-Hierarchal-Clustering └── 00-Hierarchal-Clustering-Example.ipynb ├── 21-Hierarchical-Clustering ├── 00-Hierarchical-Clustering.ipynb └── Basics.ipynb ├── 22-DBSCAN ├── 00-DBSCAN.ipynb ├── 01-DBSCAN-Hyperparameters.ipynb ├── 02-DBSCAN-Project-Exercise.ipynb ├── 03-DBSCAN-Project-Solutions.ipynb └── Basics.ipynb ├── 23-PCA-Principal-Component-Analysis ├── 00-PCA-Manual-Implementation.ipynb ├── 01-PCA-Scikit-Learn.ipynb ├── 02-PCA-Exercise-Project.ipynb ├── 03-PCA-Exercise-Project-Solutions.ipynb └── Basics.ipynb ├── 24-Model-Deployment ├── 00-Model-Persistence.ipynb ├── 01-Serving-Model-as-API.ipynb ├── Finale.ipynb ├── api.py ├── col_names.pkl ├── column_names.pkl ├── 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