Python Machine Learning Notebooks (Tutorial style)

Dr. Tirthajyoti Sarkar, Fremont, CA (Please feel free to add me on LinkedIn here)

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Requirements

• Python 3.5+
• NumPy ($ pip install numpy)
• Pandas ($ pip install pandas)
• Scikit-learn ($ pip install scikit-learn)
• SciPy ($ pip install scipy)
• Statsmodels ($ pip install statsmodels)
• MatplotLib ($ pip install matplotlib)
• Seaborn ($ pip install seaborn)
• Sympy ($ pip install sympy)

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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 perform linear regression in Python and their speed comparison (check the article I wrote on freeCodeCamp)

• Multi-variate regression with regularization

• Polynomial regression using 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)

• Detailed visual analytics and goodness-of-fit diagnostic tests for a linear regression problem

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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

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Simple deployment examples (serving ML models on web API)

• Serving a linear regression model through a simple HTTP server interface. User needs to request predictions by executing a Python script. Uses Flask and Gunicorn.

• Serving a recurrent neural network (RNN) through a HTTP webpage, complete with a web form, where users can input parameters and click a button to generate text based on the pre-trained RNN model. Uses Flask, Jinja, Keras/TensorFlow, WTForms.