Data mining and machine learning software | Free statistical software

Mlpy

mlpy is a Python, open-source, machine learning library built on top of NumPy/SciPy, the GNU Scientific Library and it makes an extensive use of the Cython language. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3. Suited for general-purpose machine learning tasks, mlpy's motivating application field is bioinformatics, i.e. the analysis of high throughput omics data. (Wikipedia).

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MLOps: MLflow Hands On, Session 2, part 2

About MLflow Code example Package model & environment Configurations Model flavours App example Better way: mlflow.pyfunc Model wrapper Packaging

From playlist ML Ops (hands-on)

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(ML 10.2) Posterior for linear regression (part 1)

How to compute the posterior distribution for the weight vector w under a Bayesian model for linear regression.

From playlist Machine Learning

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(ML 10.6) Predictive distribution for linear regression (part 3)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

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(ML 10.4) Predictive distribution for linear regression (part 1)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

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(ML 10.7) Predictive distribution for linear regression (part 4)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

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(ML 1.4) Variations on supervised and unsupervised

A broad overview. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

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(ML 10.5) Predictive distribution for linear regression (part 2)

How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.

From playlist Machine Learning

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(ML 7.7.A1) Dirichlet distribution

Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.

From playlist Machine Learning

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The Two-Dimensional Discrete Fourier Transform

The two-dimensional discrete Fourier transform (DFT) is the natural extension of the one-dimensional DFT and describes two-dimensional signals like images as a weighted sum of two dimensional sinusoids. Two-dimensional sinusoids have a horizontal frequency component and a vertical frequen

From playlist Fourier

Related pages

Logistic regression | Ridge regression | Dimensionality reduction | Infer.NET | Elastic net regularization | NumPy | SciPy | Scikit-learn | Linear discriminant analysis | Perceptron | GNU Scientific Library | Cython | Least squares | Hierarchical clustering | Principal component analysis