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
• Multi-variate regression with regularization
• Polynomial regression with how to use scikit-learn pipeline feature
• Logistic regression/classification
• k-nearest neighbor classification
• Decision trees and Random Forest
• Support vector machine classification
• K-means clustering

Function approximation by linear model and Deep Learning

• 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