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Title: GitHub - jamedrano/Machine-Learning-with-Python: Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learninghttps://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI
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Carefully curated resource links for data science in one placehttps://github.com/tirthajyoti/Data-science-best-resources
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"Some Essential Hacks and Tricks for Machine Learning with Python"https://heartbeat.fritz.ai/some-essential-hacks-and-tricks-for-machine-learning-with-python-5478bc6593f2
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#some-essential-hacks-and-tricks-for-machine-learning-with-python
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https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#essential-tutorial-type-notebooks-on-pandas-and-numpy
Detailed Numpy operationshttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_operations.ipynb
Detailed Pandas operationshttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Pandas_Operations.ipynb
Numpy and Pandas quick basicshttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Pandas_Quick.ipynb
Matplotlib and Seaborn quick basicshttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Matplotlib_Seaborn_basics.ipynb
Advanced Pandas operationshttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Advanced%20Pandas%20Operations.ipynb
How to read various data sourceshttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Read_data_various_sources/How%20to%20read%20various%20sources%20in%20a%20DataFrame.ipynb
PDF reading and table processing demohttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Read_data_various_sources/PDF%20table%20reading%20and%20processing%20demo.ipynb
How fast are Numpy operations compared to pure Python code?https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/How%20fast%20are%20NumPy%20ops.ipynb
articlehttps://towardsdatascience.com/why-you-should-forget-for-loop-for-data-science-code-and-embrace-vectorization-696632622d5f
Fast reading from Numpy using .npy file formathttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Reading.ipynb
articlehttps://towardsdatascience.com/why-you-should-start-using-npy-file-more-often-df2a13cc0161
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#tutorial-type-notebooks-covering-regression-classification-clustering-dimensionality-reduction-and-some-basic-neural-network-algorithms
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#regression
https://camo.githubusercontent.com/a20fd15968101c92a47a759f0cf49478573df42022527f586bae132e7a89e463/68747470733a2f2f736c696465706c617965722e636f6d2f736c6964652f363035333138322f32302f696d616765732f31302f53696d706c652b4c696e6561722b52656772657373696f6e2b4d6f64656c2e6a7067
Multiple ways to perform linear regression in Python and their speed comparisonhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb
check the article I wrote on freeCodeCamphttps://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b
Multi-variate regression with regularizationhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Multi-variate%20LASSO%20regression%20with%20CV.ipynb
https://camo.githubusercontent.com/19dff5115626989d03b2837408d408331b67126f9f576da8f6c55c15217c0648/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f662f66382f4c315f616e645f4c325f62616c6c732e7376672f33303070782d4c315f616e645f4c325f62616c6c732e7376672e706e67
check the article I wrote on Towards Data Sciencehttps://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49
Decision trees and Random Forest regressionhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Random_Forest_Regression.ipynb
Detailed visual analytics and goodness-of-fit diagnostic tests for a linear regression problemhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb
Robust linear regression using HuberRegressor from Scikit-learnhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Robust%20Linear%20Regression.ipynb
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#classification
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb
https://camo.githubusercontent.com/203433cd3bf1cb146836c0be530ec0166f92c17f944419ac00de15e9b5309f8a/68747470733a2f2f7170682e66732e71756f726163646e2e6e65742f6d61696e2d71696d672d3931346232396537373765373862343462363732343662363661346436643731
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/DecisionTrees_RandomForest_Classification.ipynb
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Support_Vector_Machine_Classification.ipynb
check the article I wrote in Towards Data Science on SVM and sorting algorithmhttps://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b
https://camo.githubusercontent.com/c0a88222f380708c67b7daf06ef8c4e5381fae00699fdd98c0d8aac8c0d79afd/68747470733a2f2f646f63732e6f70656e63762e6f72672f322e342f5f696d616765732f6f7074696d616c2d6879706572706c616e652e706e67
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#clustering
https://camo.githubusercontent.com/ae8d572b41d0a4833f42d57a4fffef9535ccddeaa07c50719a8218f1ca0b3bf9/68747470733a2f2f692e7974696d672e636f6d2f76692f494a74363275615a522d4d2f6d617872657364656661756c742e6a7067
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/K_Means_Clustering_Practice.ipynb
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Affinity_Propagation.ipynb
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Mean_Shift_Clustering.ipynb
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/DBScan_Clustering.ipynb
Here is the Notebookhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Hierarchical_Clustering.ipynb
https://camo.githubusercontent.com/40f3940d8287885aa32db683f3e25d90e51d00e8609bd3221f45f140836b1eab/68747470733a2f2f7777772e7265736561726368676174652e6e65742f70726f66696c652f4361727374656e5f57616c746865722f7075626c69636174696f6e2f3237333435363930362f6669677572652f666967332f41533a32393438363630363530383434313940313434373331323935363530312f4578616d706c652d6f662d68696572617263686963616c2d636c7573746572696e672d636c7573746572732d6172652d636f6e73656375746976656c792d6d65726765642d776974682d7468652d6d6f73742e706e67
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#dimensionality-reduction
https://camo.githubusercontent.com/af1155a85ba16ad613e2ad0a19eeb579a8cb413d377ca35586a329e7a86baebd/68747470733a2f2f692e7974696d672e636f6d2f76692f5150343349792d515157592f6d617872657364656661756c742e6a7067
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#deep-learningneural-network
Demo notebook to illustrate the superiority of deep neural network for complex nonlinear function approximation taskhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Function%20Approximation%20by%20Neural%20Network/Polynomial%20regression%20-%20linear%20and%20neural%20network.ipynb
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#random-data-generation-using-symbolic-expressions
Sympy packagehttps://www.sympy.org/en/index.html
Random regression and classification problem generation with symbolic expressionhttps://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#synthetic-data-generation-techniques
Notebooks herehttps://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Synthetic_data_generation
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#simple-deployment-examples-serving-ml-models-on-web-api
Serving a linear regression model through a simple HTTP server interfacehttps://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/Linear_regression
Serving a recurrent neural network (RNN) through a HTTP webpagehttps://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/rnn_app
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#object-oriented-programming-with-machine-learning
building your own Scikit-learn-like estimator, and making it betterhttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.ipynb
Object-oriented programming for data scientists: Build your ML estimatorhttps://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64
How a simple mix of object-oriented programming can sharpen your deep learning prototypehttps://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#unit-testing-ml-code-with-pytest
Pytesthttps://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Pytest
https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#memory-and-timing-profiling
Memory profling using Scalenehttps://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Memory-profiling/Scalene
Time-profiling data science codehttps://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Time-profiling/cProfile.ipynb
machine-learning-with-python.readthedocs.io/en/latest/https://machine-learning-with-python.readthedocs.io/en/latest/
Readme https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#readme-ov-file
BSD-2-Clause license https://patch-diff.githubusercontent.com/jamedrano/Machine-Learning-with-Python#BSD-2-Clause-1-ov-file
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