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