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Title: GitHub - sudevpdas/Python-Machine-Learning-Demo-Tutorials: Practice and tutorial-style notebooks covering wide variety of machine learning techniques · GitHub

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Description: Practice and tutorial-style notebooks covering wide variety of machine learning techniques - sudevpdas/Python-Machine-Learning-Demo-Tutorials

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	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/commits/master/
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Function Approximation by Neural Network	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/tree/master/Function%20Approximation%20by%20Neural%20Network
Function Approximation by Neural Network	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/tree/master/Function%20Approximation%20by%20Neural%20Network
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Pandas and Numpy	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/tree/master/Pandas%20and%20Numpy
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Linear_Regression_Methods.ipynb	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/Linear_Regression_Methods.ipynb
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Linear_Regression_Practice.ipynb	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/Linear_Regression_Practice.ipynb
Multi-variate LASSO regression with CV.ipynb	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/Multi-variate%20LASSO%20regression%20with%20CV.ipynb
Multi-variate LASSO regression with CV.ipynb	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/Multi-variate%20LASSO%20regression%20with%20CV.ipynb
README.md	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/README.md
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Regularized polynomial regression with linear and random sampling.ipynb	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/Regularized%20polynomial%20regression%20with%20linear%20and%20random%20sampling.ipynb
Regularized polynomial regression with linear and random sampling.ipynb	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials/blob/master/Regularized%20polynomial%20regression%20with%20linear%20and%20random%20sampling.ipynb
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README	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials
BSD-2-Clause license	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#python-machine-learning-notebooks
You can connect with me on LinkedIn here	https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#dr-tirthajyoti-sarkar-sunnyvale-ca-you-can-connect-with-me-on-linkedin-here
	https://camo.githubusercontent.com/b2c28c2b6348745383833a6eeec89096b35b613db41a33df6d16ec50be46f259/687474703a2f2f7777772e627573696e657373636f6d707574696e67776f726c642e636f2e756b2f77702d636f6e74656e742f75706c6f6164732f323031372f31322f4d616368696e652d4c6561726e696e672e6a7067
"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://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#some-essential-hacks-and-tricks-for-machine-learning-with-python
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#essential-tutorial-type-notebooks-on-pandas-and-numpy
Basics of Numpy array	https://github.com/tirthajyoti/PythonMachineLearning/blob/master/Pandas%20and%20Numpy/Basics%20of%20Numpy%20arrays.ipynb
Basics of Pandas DataFrame	https://github.com/tirthajyoti/PythonMachineLearning/blob/master/Pandas%20and%20Numpy/Basics%20of%20Pandas%20DataFrame.ipynb
Basics of Matplotlib and Descriptive Statistics	https://github.com/tirthajyoti/PythonMachineLearning/blob/master/Pandas%20and%20Numpy/Basics%20of%20Matplotlib%20and%20Descriptive%20Statistics.ipynb
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#tutorial-type-notebooks-covering-regression-classification-clustering-dimensionality-reduction-and-some-basic-neural-network-algorithms
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#regression
	https://camo.githubusercontent.com/a20fd15968101c92a47a759f0cf49478573df42022527f586bae132e7a89e463/68747470733a2f2f736c696465706c617965722e636f6d2f736c6964652f363035333138322f32302f696d616765732f31302f53696d706c652b4c696e6561722b52656772657373696f6e2b4d6f64656c2e6a7067
check the article I wrote on freeCodeCamp	https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b
	https://camo.githubusercontent.com/19dff5115626989d03b2837408d408331b67126f9f576da8f6c55c15217c0648/68747470733a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f7468756d622f662f66382f4c315f616e645f4c325f62616c6c732e7376672f33303070782d4c315f616e645f4c325f62616c6c732e7376672e706e67
check the article I wrote on Towards Data Science	https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#classification
	https://camo.githubusercontent.com/203433cd3bf1cb146836c0be530ec0166f92c17f944419ac00de15e9b5309f8a/68747470733a2f2f7170682e66732e71756f726163646e2e6e65742f6d61696e2d71696d672d3931346232396537373765373862343462363732343662363661346436643731
check the article I wrote in Towards Data Science on SVM and sorting algorithm	https://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b
	https://camo.githubusercontent.com/c0a88222f380708c67b7daf06ef8c4e5381fae00699fdd98c0d8aac8c0d79afd/68747470733a2f2f646f63732e6f70656e63762e6f72672f322e342f5f696d616765732f6f7074696d616c2d6879706572706c616e652e706e67
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#clustering
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	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#dimensionality-reduction
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	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#deep-learningneural-network
	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#random-data-generation-using-symbolic-expressions
Sympy package	https://www.sympy.org/en/index.html
Random regression and classification problem generation with symbolic expression	https://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d
Readme	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#readme-ov-file
BSD-2-Clause license	https://github.com/sudevpdas/Python-Machine-Learning-Demo-Tutorials#BSD-2-Clause-1-ov-file
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