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Title: GitHub - tpopenfoose/PythonMachineLearning: 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 - tpopenfoose/PythonMachineLearning

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LogisticRegressionTitanic.ipynb	https://github.com/tpopenfoose/PythonMachineLearning/blob/master/LogisticRegressionTitanic.ipynb
Multi-variate LASSO regression with CV.ipynb	https://github.com/tpopenfoose/PythonMachineLearning/blob/master/Multi-variate%20LASSO%20regression%20with%20CV.ipynb
Multi-variate LASSO regression with CV.ipynb	https://github.com/tpopenfoose/PythonMachineLearning/blob/master/Multi-variate%20LASSO%20regression%20with%20CV.ipynb
Naive_Bayes_Practice.ipynb	https://github.com/tpopenfoose/PythonMachineLearning/blob/master/Naive_Bayes_Practice.ipynb
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README	https://github.com/tpopenfoose/PythonMachineLearning
BSD-2-Clause license	https://github.com/tpopenfoose/PythonMachineLearning
	https://github.com/tpopenfoose/PythonMachineLearning#python-machine-learning-notebooks
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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://github.com/tpopenfoose/PythonMachineLearning#some-essential-hacks-and-tricks-for-machine-learning-with-python
	https://github.com/tpopenfoose/PythonMachineLearning#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/tpopenfoose/PythonMachineLearning#tutorial-type-notebooks-covering-regression-classification-clustering-dimensionality-reduction-and-some-basic-neural-network-algorithms
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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
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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/tpopenfoose/PythonMachineLearning#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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	https://github.com/tpopenfoose/PythonMachineLearning#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
add me on LinkedIn here	https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/
Readme	https://github.com/tpopenfoose/PythonMachineLearning#readme-ov-file
BSD-2-Clause license	https://github.com/tpopenfoose/PythonMachineLearning#BSD-2-Clause-1-ov-file
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