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Title: GitHub - xguo7013/Machine-Learning-with-Python: 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 - xguo7013/Machine-Learning-with-Python

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Business_optimization	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Business_optimization
Classification	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Classification
Classification	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Classification
Clustering-Dimensionality-Reduction	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Clustering-Dimensionality-Reduction
Clustering-Dimensionality-Reduction	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Clustering-Dimensionality-Reduction
Complexity_Learning_curves	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Complexity_Learning_curves
Complexity_Learning_curves	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Complexity_Learning_curves
Datasets	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Datasets
Datasets	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Datasets
Deployment	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Deployment
Deployment	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Deployment
Function Approximation by Neural Network	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Function%20Approximation%20by%20Neural%20Network
Function Approximation by Neural Network	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Function%20Approximation%20by%20Neural%20Network
Images	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Images
Images	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Images
OOP_in_ML	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/OOP_in_ML
OOP_in_ML	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/OOP_in_ML
Pandas and Numpy	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Pandas%20and%20Numpy
Pandas and Numpy	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Pandas%20and%20Numpy
Pytest	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Pytest
Pytest	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Pytest
Random Function Generator	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Random%20Function%20Generator
Random Function Generator	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Random%20Function%20Generator
Real Dataset Problems	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Real%20Dataset%20Problems
Real Dataset Problems	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Real%20Dataset%20Problems
Regression	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Regression
Regression	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Regression
Streamlit-ML-apps	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Streamlit-ML-apps
Streamlit-ML-apps	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Streamlit-ML-apps
Synthetic_data_generation	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Synthetic_data_generation
Synthetic_data_generation	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Synthetic_data_generation
Utilities	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Utilities
Utilities	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/Utilities
docs	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/docs
docs	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master/docs
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Interactive ML-1.ipynb	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/Interactive%20ML-1.ipynb
Interactive ML-1.ipynb	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/Interactive%20ML-1.ipynb
Jupyter_Markdown_Primer.ipynb	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/Jupyter_Markdown_Primer.ipynb
Jupyter_Markdown_Primer.ipynb	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/Jupyter_Markdown_Primer.ipynb
LICENSE	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/LICENSE
LICENSE	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/LICENSE
README.md	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/README.md
README.md	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/README.md
_config.yml	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/_config.yml
_config.yml	https://github.com/xguo7013/Machine-Learning-with-Python/blob/master/_config.yml
README	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master
BSD-2-Clause license	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master
	https://opensource.org/licenses/BSD-2-Clause
	https://github.com/tirthajyoti/Machine-Learning-with-Python/network
	https://github.com/tirthajyoti/Machine-Learning-with-Python/stargazers
	https://github.com/tirthajyoti/Machine-Learning-with-Python/pulls
ML website	https://machine-learning-with-python.readthedocs.io/en/latest/
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#python-machine-learning-jupyter-notebooks-ml-website
Please feel free to connect on LinkedIn here	https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#dr-tirthajyoti-sarkar-fremont-california-please-feel-free-to-connect-on-linkedin-here
	https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Images/ML-DS-cycle-1.png
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#also-check-out-these-super-useful-repos-that-i-curated
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning	https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#highly-cited-and-useful-papers-related-to-machine-learning-deep-learning-ai-game-theory-reinforcement-learning
Carefully curated resource links for data science in one place	https://github.com/tirthajyoti/Data-science-best-resources
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#carefully-curated-resource-links-for-data-science-in-one-place
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#requirements
"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/xguo7013/Machine-Learning-with-Python/tree/master#some-essential-hacks-and-tricks-for-machine-learning-with-python
	https://camo.githubusercontent.com/83beec85483ed4954a90e45f0c113c5ecead75a4e2e0ebe8e7286fe1f7aff7df/68747470733a2f2f636f6f6b69656567726f75702e636f6d2f77702d636f6e74656e742f75706c6f6164732f323031382f31302f322d312e706e67
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#essential-tutorial-type-notebooks-on-pandas-and-numpy
Detailed Numpy operations	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_operations.ipynb
Detailed Pandas operations	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Pandas_Operations.ipynb
Numpy and Pandas quick basics	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Pandas_Quick.ipynb
Matplotlib and Seaborn quick basics	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Matplotlib_Seaborn_basics.ipynb
Advanced Pandas operations	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Advanced%20Pandas%20Operations.ipynb
How to read various data sources	https://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 demo	https://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
article	https://towardsdatascience.com/why-you-should-forget-for-loop-for-data-science-code-and-embrace-vectorization-696632622d5f
Fast reading from Numpy using .npy file format	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Reading.ipynb
article	https://towardsdatascience.com/why-you-should-start-using-npy-file-more-often-df2a13cc0161
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#tutorial-type-notebooks-covering-regression-classification-clustering-dimensionality-reduction-and-some-basic-neural-network-algorithms
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#regression
	https://camo.githubusercontent.com/a20fd15968101c92a47a759f0cf49478573df42022527f586bae132e7a89e463/68747470733a2f2f736c696465706c617965722e636f6d2f736c6964652f363035333138322f32302f696d616765732f31302f53696d706c652b4c696e6561722b52656772657373696f6e2b4d6f64656c2e6a7067
Multiple ways to perform linear regression in Python and their speed comparison	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb
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
Multi-variate regression with regularization	https://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 Science	https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49
Decision trees and Random Forest regression	https://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 problem	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb
Robust linear regression using HuberRegressor from Scikit-learn	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Robust%20Linear%20Regression.ipynb
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#classification
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb
	https://camo.githubusercontent.com/203433cd3bf1cb146836c0be530ec0166f92c17f944419ac00de15e9b5309f8a/68747470733a2f2f7170682e66732e71756f726163646e2e6e65742f6d61696e2d71696d672d3931346232396537373765373862343462363732343662363661346436643731
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/DecisionTrees_RandomForest_Classification.ipynb
Here is the Notebook	https://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 algorithm	https://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b
	https://camo.githubusercontent.com/c0a88222f380708c67b7daf06ef8c4e5381fae00699fdd98c0d8aac8c0d79afd/68747470733a2f2f646f63732e6f70656e63762e6f72672f322e342f5f696d616765732f6f7074696d616c2d6879706572706c616e652e706e67
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#clustering
	https://camo.githubusercontent.com/ae8d572b41d0a4833f42d57a4fffef9535ccddeaa07c50719a8218f1ca0b3bf9/68747470733a2f2f692e7974696d672e636f6d2f76692f494a74363275615a522d4d2f6d617872657364656661756c742e6a7067
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/K_Means_Clustering_Practice.ipynb
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Affinity_Propagation.ipynb
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Mean_Shift_Clustering.ipynb
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/DBScan_Clustering.ipynb
Here is the Notebook	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Hierarchical_Clustering.ipynb
	https://camo.githubusercontent.com/40f3940d8287885aa32db683f3e25d90e51d00e8609bd3221f45f140836b1eab/68747470733a2f2f7777772e7265736561726368676174652e6e65742f70726f66696c652f4361727374656e5f57616c746865722f7075626c69636174696f6e2f3237333435363930362f6669677572652f666967332f41533a32393438363630363530383434313940313434373331323935363530312f4578616d706c652d6f662d68696572617263686963616c2d636c7573746572696e672d636c7573746572732d6172652d636f6e73656375746976656c792d6d65726765642d776974682d7468652d6d6f73742e706e67
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#dimensionality-reduction
	https://camo.githubusercontent.com/af1155a85ba16ad613e2ad0a19eeb579a8cb413d377ca35586a329e7a86baebd/68747470733a2f2f692e7974696d672e636f6d2f76692f5150343349792d515157592f6d617872657364656661756c742e6a7067
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#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://github.com/xguo7013/Machine-Learning-with-Python/tree/master#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
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#synthetic-data-generation-techniques
Notebooks here	https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Synthetic_data_generation
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#simple-deployment-examples-serving-ml-models-on-web-api
Serving a linear regression model through a simple HTTP server interface	https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/Linear_regression
Serving a recurrent neural network (RNN) through a HTTP webpage	https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/rnn_app
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#object-oriented-programming-with-machine-learning
building your own Scikit-learn-like estimator, and making it better	https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.ipynb
Object-oriented programming for data scientists: Build your ML estimator	https://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 prototype	https://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd
	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#unit-testing-ml-code-with-pytest
Pytest	https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Pytest
machine-learning-with-python.readthedocs.io/en/latest/	https://machine-learning-with-python.readthedocs.io/en/latest/
Readme	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#readme-ov-file
BSD-2-Clause license	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master#BSD-2-Clause-1-ov-file
Please reload this page	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master
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Please reload this page	https://github.com/xguo7013/Machine-Learning-with-Python/tree/master
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