Dimensionality-reduction Libraries

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Dimensionality-reduction Libraries

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

You can start with this article that I wrote in Heartbeat magazine (on Medium platform): Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, …

Machine Learning notebooks for refreshing concepts.

Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet. Linear & Multiple Regression Logistic Regression Regularization

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Required Files: Linear regression is a linear approach to model the relationship between a scalar response (dependent varaible) and one or more explanatory variables (independent variables). The case…

Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python

In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.

The prerequisites to better understand the code and concept are: Since, the initial two principal COmponents have high variance. so, we will select the initial two PC'S. The time taken for classifica…

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