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.

• Basics of Numpy array
• Basics of Pandas DataFrame
• Basics of Matplotlib and Descriptive Statistics

Tutorial-type notebooks covering regression, classification, clustering, dimensionality reduction, and some basic neural network algorithms

Regression

• 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)
• 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

• Logistic regression/classification

• k-nearest neighbor classification
• Decision trees and Random Forest Classification
• Support vector machine classification (check the article I wrote in Towards Data Science on SVM and sorting algorithm)

• Naive Bayes classification

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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

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Dimensionality reduction

• Principal component analysis

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Deep Learning/Neural Network

• 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

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Random data generation using symbolic expressions

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

Here is my article on Medium on this topic: Random regression and classification problem generation with symbolic expression