Data mining and machine learning software | Free statistical software
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).
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)
(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
(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
(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
(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
(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
(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
(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
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