DURGASHIVAPRASAD / Machine-Learning-with-Python

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Python Machine Learning Notebooks (Tutorial style)

Dr. Tirthajyoti Sarkar, Sunnyvale, CA (Please feel free to add 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