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Algorithms and Data Structures for Data Science and Machine Learning (Algorithms and Data Structures Simplified) Part 2

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Volkan Sonmez's Machine Learning Projects

© 2018 - current, Volkan Sonmez, www.pythonicfool.com

This is a repository of teaching materials, code, and data for my data analysis and machine learning projects.

Each repository will (usually) correspond to one of the posts on my website.

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  • Share—copy and redistribute the material in any medium or format
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  • Attribution—You must give appropriate credit (mentioning that your work is derived from work that is © Volkan Sonmez and, where practical, linking to http://www.pythonicfool.com/), and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. LICENSE

Algorithms-and-Data-Structures-2

Algorithms and Data Structures for Data Science and Machine Learning (Algorithms and Data Structures Simplified)

  • MST Algorithms:

A minimum spanning tree (MST) or minimum weight spanning tree is a subset of the edges of a connected, edge-weighted undirected graph that connects all the vertices together. Prim's and Kruskal's algorithms are analyzed in this notebook to find MST.

  • Sorting Algorithms:

Merge Sort- n*logn, Quick Sort- n *logn, Bubble Sort- npow(2)

  • Basics for Convolution & Intro to Digital Signal Processing:

Basics of Convolution Notebook (array and matrix convolutions and FFT)

  • DFT, FFT, and Convolution of Signals:

Polynomial Multiplication with DFT, FFT from scratch, and Convolution of Signals

  • Network Flow Problems, Bipartite Matching:

Maximizing the network flow, bipartiteness testing, bipartite matching, and visualization of graphs with matplotlib.

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