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Python Machine Learning Notebooks (Tutorial style)

Python Machine Learning Notebooks (Tutorial style)

Dr. Tirthajyoti Sarkar, Sunnyvale, CA (You can connect with me on LinkedIn here)

Essential codes/demo IPython notebooks for jump-starting machine learning/data science.
You can start with this article that I wrote in Heartbeat magazine (on Medium platform):

"Some Essential Hacks and Tricks for Machine Learning 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, dimensionality reduction, and some basic neural network algorithms

Regression

  • Simple linear regression with t-statistic generation
  • Polynomial regression with how to use scikit-learn pipeline feature (check the article I wrote on Towards Data Science)
  • Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)

Classification

  • Logistic regression/classification
  • Naive Bayes classification

Clustering

  • K-means clustering
  • Affinity propagation (showing its time complexity and the effect of damping factor)
  • Mean-shift technique (showing its time complexity and the effect of noise on cluster discovery)
  • DBSCAN (showing how it can generically detect areas of high density irrespective of cluster shapes, which the k-means fails to do)
  • Hierarchical clustering with Dendograms showing how to choose optimal number of clusters

Dimensionality reduction

  • Principal component analysis

Deep Learning/Neural Network

Random data generation using symbolic expressions

  • How to use Sympy package to generate random datasets using symbolic mathematical expressions.
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