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Title: GitHub - debidash2007/Machine-Learning-with-Python: Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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Description: Practice and tutorial-style notebooks covering wide variety of machine learning techniques - debidash2007/Machine-Learning-with-Python

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Function Approximation by Neural Network	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python/tree/master/Function%20Approximation%20by%20Neural%20Network
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Pandas and Numpy	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python/tree/master/Pandas%20and%20Numpy
Pandas and Numpy	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python/tree/master/Pandas%20and%20Numpy
Random Function Generator	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python/tree/master/Random%20Function%20Generator
Random Function Generator	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python/tree/master/Random%20Function%20Generator
Real Dataset Problems	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python/tree/master/Real%20Dataset%20Problems
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	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#python-machine-learning-notebooks-tutorial-style
Please feel free to add me on LinkedIn here	https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7
	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#dr-tirthajyoti-sarkar-sunnyvale-ca-please-feel-free-to-add-me-on-linkedin-here
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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/debidash2007/Machine-Learning-with-Python#some-essential-hacks-and-tricks-for-machine-learning-with-python
	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#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://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#tutorial-type-notebooks-covering-regression-classification-clustering-dimensionality-reduction-and-some-basic-neural-network-algorithms
	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#regression
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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://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#classification
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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
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	https://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#deep-learningneural-network
Demo notebook to illustrate the superiority of deep neural network for complex nonlinear function approximation task	https://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/debidash2007/Machine-Learning-with-Python#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://patch-diff.githubusercontent.com/debidash2007/Machine-Learning-with-Python#readme-ov-file
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	https://github.com
Terms	https://docs.github.com/site-policy/github-terms/github-terms-of-service
Privacy	https://docs.github.com/site-policy/privacy-policies/github-privacy-statement
Security	https://github.com/security
Status	https://www.githubstatus.com/
Community	https://github.community/
Docs	https://docs.github.com/
Contact	https://support.github.com?tags=dotcom-footer

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URLs of crawlers that visited me.