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Title: GitHub - streamnsight/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 - streamnsight/Machine-Learning-with-Python

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Function Approximation by Neural Network	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python/tree/master/Function%20Approximation%20by%20Neural%20Network
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Pandas and Numpy	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python/tree/master/Pandas%20and%20Numpy
Pandas and Numpy	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python/tree/master/Pandas%20and%20Numpy
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README	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python
BSD-2-Clause license	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python
	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python#python-machine-learning-notebooks-tutorial-style
You can connect with me on LinkedIn here	https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7
	https://patch-diff.githubusercontent.com/streamnsight/Machine-Learning-with-Python#dr-tirthajyoti-sarkar-sunnyvale-ca-you-can-connect-with-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/streamnsight/Machine-Learning-with-Python#some-essential-hacks-and-tricks-for-machine-learning-with-python
	https://patch-diff.githubusercontent.com/streamnsight/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/streamnsight/Machine-Learning-with-Python#tutorial-type-notebooks-covering-regression-classification-clustering-dimensionality-reduction-and-some-basic-neural-network-algorithms
	https://patch-diff.githubusercontent.com/streamnsight/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/streamnsight/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
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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/streamnsight/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
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