Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
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Updated
Jul 20, 2024 - Jupyter Notebook
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
A Jupyter Notebook that implements in Python 3 the Eigenfaces algorithm for face recognition
[SOML'21] Reference material, tutorial notebooks and other resources for Summer of ML, organised by AIMLC IITD.
A small R notebook describing how plot common (aDNA) population genetics PCAs using R and the tidyverse.
Blank notebooks and solutions for the Mathematics for Machine Learning specialization by Imperial College of London on Coursera.
Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks
In this notebook, I compared two famous clustering algorithm, the minibatchkmeans and the regular kmeans on cellular image dataset.
Machine Learning exercises in Python (Jupyter notebooks)
Jupyter notebooks with notes, code, and exercises from Linear Algebra: Theory, Intuition, Code by Mike X Cohen (2021).
Repo with my most popular kaggle notebooks. I've put a lot of effort into them back in the day, so they are highly curated and well documented.
This Notebook illustrate the calculation of Semantic Similarity using WordNet Embedding and Principal Component Analysis
This Repo contains Notebook for Anomaly Detection using Dimensionality Reduction techniques
A notebook using many unsupervised learning techniques. PCA, K-means, Gaussian Mixtures. Clustering, dimensionality reduction, anomaly detection
Implementation of a Hybrid Hypergraph Attention Network for hyperspectral image classification. Includes data preprocessing with PCA and SLIC, scripts for data downloading, training, evaluation, and Jupyter notebooks for experimentation. Uses DL frameworks and scientific computing libraries.
Jupiter notebook with EEG-data classification problem from the MNE library
NBA players clustering and Points prediction
A series of 12 assignments/labs regarding Stochastic Processes and Machine Learning including a plethora of models and techniques implemented in Google Colab notebooks
In this repository, I have displayed some of the datasets I've worked upon.
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