Computational biology PhD researcher. Interested in science, software development, and machine learning. I write about medical research at BioSky.co and contribute content to a variety of additional publications.CVAbout
One of the best things about the iPython notebook is the number of easy-to-follow tutorials it has inspired. I thought I’d share a few that I’ve found on machine learning and statistics.
- Python for Developers – great resource for those wanting to learn and/or deepen their understanding of Python.
- Machine Learning with scikit-learn – provides a good introduction and background to machine learning.
- Machine learning with Python – covers regression, neural networks, decision trees.
- Machine Learning with Python – covers PCA, k-means clustering, k-nearest neighbours.
- Learn Data Science with Python – covers regression, random forests, k-means clustering.
- Probabilistic Programming & Bayesian Methods for Hackers – covers Bayesian methods including Markov Chain Monte Carlo.
- Bayesian data analysis – covers how probabilistic programming works.
- Supervised Learning SVM – covers Support Vector Machines (SVM)
- Face Recognition– covers PCA, and SVM.
- Particle Filter – covers the identification and tracking of objects in a video.
I’ll continue to update the list as I find new notebooks I find handy.
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