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# Python Machine Learning Notebooks
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Essential codes for jump-starting machine learning/data science with Python
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### Essential tutorial-type notebooks on Pandas and Numpy
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## Essential tutorial-type notebooks on Pandas and Numpy
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* Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, matplotlib etc.
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### Tutorial-type notebooks covering regression, classification, clustering, and some basic neural network algorithms
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## Tutorial-type notebooks covering regression, classification, clustering, and some basic neural network algorithms
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### Regression
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* Simple linear regression with t-statistic generation
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* Multiple ways to do linear regression in Python and their speed comparison ([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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* Multi-variate regression with regularization
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* Polynomial regression with how to use ***scikit-learn pipeline feature*** ([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))
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* Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)
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### Classification
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* Logistic regression/classification
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* _k_-nearest neighbor classification
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* Decision trees and Random Forest Classification
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* Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)
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* Support vector machine classification
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* Naive Bayes classification
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### Clustering
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* _K_-means clustering
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### Function approximation by linear model and Deep Learning method
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* Demo notebook to illustrate the superiority of deep neural network for complex nonlinear function approximation task.
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* Step-by-step building of 1-hidden-layer and 2-hidden-layer dense network using basic TensorFlow methods
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### Basic interactive controls demo
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## Basic interactive controls demo
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* Demo on how to integrate basic interactive controls (slider bars, drop-down menus, check-boxes etc.) in a Jupyter notebook and use them for interactive machine learning task
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## Run Jupyter using Docker

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