Some of the algorithms developed in this repository are from the courses of the Masters degree in statistics for data science at the uc3m and some others from the book Machine Learning from scratch of the author Jason Brownlee. This book offers algorithms in raw python code with the aim of learning the mathematics behind the algorithms. There are some other algorithms from leet code and finally, own creations.
The aim of this repository is not to offer the algorithms to users, it is pure didactical for myself, practice different programming languages such as C++, R, Python and Julia.
(Currently building the repository)
List of folders:
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Leetcode algorithms:
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Data Structures algorithms:
- Power of square matrix (R, Python)
- Split dataframe function (R)
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Machine Learning Supervised algorithms:
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Machine Learning Unsupervised algorithms:
- K-means algorithm (R, Python)
- Knn algorithm (R, Python)
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Neural Networks algorithms:
- Multilayer perceptron algorihm (R, Python)
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Kernel Density algorithms:
- Moving Histogram (R)
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Statistical learning algorihtms:
- Simple Linear regression model (R, Python)
- Multivariate linear regression model (R, Python)
- Logistic regression model (R)
- Finite-state Discrete Markov Chain n_states vector (R)
- Finite-state Discrete Markov Chain n_step vector (R)
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Bayesian Algorithms:
- Gibbs sampling algorithm (R, Python)
- MCMC Metropolis algorithm (R, Python)
- MCMC Metropolis-Hastings algorithm (R, Python)
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Other algorithms (own creations):
- Godel, Escher and Bach, MIMU problem algorithm (R)
- Roman numbers algorithm, get the roman given positive integer (Python)
- Triangular unordered matrix (R)
- Fibonacci series algorithm, get the nth Fibonacci number given its position (Python)