├── .gitignore ├── LICENSE ├── README.md ├── data └── Create_Higgs_CSV.ipynb ├── notebooks ├── Big_Data │ └── Zeppelin │ │ ├── 00_Whirlwind_Tour_of_Zeppelin.json │ │ ├── 01_File_Based_Data_Sources.json │ │ ├── 03_Spark_Fundamental_Structured_Operations.json │ │ ├── Machine_Learning_Zeppelin.json │ │ ├── Spark_Machine_Learning.json │ │ ├── Spark_Tutorial.json │ │ └── Supervised_Machine_Learning.json ├── Checksum │ └── Longitudinal_Redundancy_Check.ipynb ├── Cryptography │ └── 1_Caesar_Shift.ipynb ├── Hashing │ ├── Birthday_Attack.ipynb │ └── Intro_to_Hashing.ipynb ├── Information_Theory │ ├── Hamming_Distance.ipynb │ └── Stirlings_Approximation_Factorial.ipynb ├── Legacy_Code │ └── Bernoulli_Naive_Bayes.ipynb ├── Machine_Learning │ ├── Algorithms_From_Scratch │ │ ├── Bagging_and_Bootstrapping.ipynb │ │ ├── Bernoulli_Naive_Bayes.ipynb │ │ ├── Cross-Validation.ipynb │ │ ├── Gradient_Descent.ipynb │ │ ├── K-means.ipynb │ │ ├── KNN.ipynb │ │ ├── PCA.ipynb │ │ ├── Train_Test_Split.ipynb │ │ └── Train_Validation_Test_Split.ipynb │ ├── Fourier_Transforms │ │ └── FFT.ipynb │ ├── Supervised_Learning │ │ ├── Advanced_Techniques │ │ │ └── Bagging │ │ │ │ └── Bagging_Simulation.ipynb │ │ ├── Classification │ │ │ ├── EDA │ │ │ │ └── Classification_EDA.ipynb │ │ │ ├── Logistic_Regression │ │ │ │ ├── Logistic_Regression_Sklearn_Example.ipynb │ │ │ │ └── Sigmoid_Function.ipynb │ │ │ └── Support_Vector_Machines │ │ │ │ └── Basics_of_SVMs.ipynb │ │ ├── Model_Selection │ │ │ ├── Feature_Selection.ipynb │ │ │ ├── Model_Tuning_and_Cross_Validation.ipynb │ │ │ └── Train_Test_Split.ipynb │ │ ├── Numerical_Methods │ │ │ └── Gradient_Descent │ │ │ │ └── Linear_Regression_&_Intro_to_GD.ipynb │ │ └── Regression │ │ │ └── Linear_Regression │ │ │ ├── 1_Linear_Regression_101.ipynb │ │ │ ├── 2_Linear_Regression_Metrics.ipynb │ │ │ ├── 3_Linear_Regression_Assumptions_and_Evaluation.ipynb │ │ │ └── 4_Linear_Regression_EDA_and_Residual_Plots.ipynb │ └── Unsupervised_Learning │ │ ├── Clustering │ │ └── Kmeans │ │ │ └── Kmeans_Clustering_w_Sklearn.ipynb │ │ └── SVD │ │ ├── SVD_to_image.ipynb │ │ └── SVD_with_Iris_and_Images.ipynb ├── OS_library │ └── OS_Sandbox.ipynb ├── PyTorch │ └── PyTorch_Intro.ipynb ├── Python │ ├── Coding_Best_Practices │ │ ├── Complexity_&_Big_O.ipynb │ │ ├── Complexity_Practice.ipynb │ │ └── OOP_How_to_Write_a_Class.ipynb │ ├── Demos │ │ ├── ML_101.ipynb │ │ ├── Python_101_Instructor.ipynb │ │ └── Python_101_Student.ipynb │ ├── NumPy │ │ ├── NP_argwhere_isin.ipynb │ │ └── Numpy_concat_append_ravel_mgrid.ipynb │ ├── Programming_Problems │ │ ├── Alphabet_Magnets.ipynb │ │ ├── Bubble_Sort.ipynb │ │ ├── Cryptogram_Puzzle.ipynb │ │ ├── Dot_Product.ipynb │ │ ├── Factorial.ipynb │ │ ├── Fibonacci.ipynb │ │ ├── GCD_Problem.ipynb │ │ ├── Guess_a_Number.ipynb │ │ ├── Moving_Average_Problem.ipynb │ │ ├── OLS_R2_and_adjR2.ipynb │ │ ├── Project_Euler_Problem_8_with_Deque.ipynb │ │ ├── Reverse_String.ipynb │ │ ├── Rotation_Problem.ipynb │ │ └── SSE.ipynb │ ├── Python_Internals │ │ ├── Arrays_vs_Lists.ipynb │ │ ├── Deep_vs_Shallow_Copying.ipynb │ │ ├── Pickling.ipynb │ │ ├── Python_Data_Structure_Comparison.ipynb │ │ ├── Working_with_Bits.ipynb │ │ └── listexp_vs_genexp.ipynb │ ├── Recursion │ │ └── Factorial_&_Fibonacci.ipynb │ └── Visualizations │ │ ├── Boxplots.ipynb │ │ ├── Matplotlib_Tutorial.ipynb │ │ └── Seaborn_Visualizations_&_Data.ipynb ├── Random_Number_Generators │ ├── 1_PRNG_Middle_Square_Method.ipynb │ ├── 2_PRNG_Linear_Congruential_Generator.ipynb │ ├── 3_PRNG_Linear_Feedback_Shift_Register.ipynb │ └── 4_PRNG_Dev_Random_Directory.ipynb └── Statistics │ └── Central_Limit_Theorem.ipynb └── pkl_files ├── subset_df.pkl └── subset_df2.pkl /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 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