It contains 2 notebooks: a general Theano neural networks tutorial, and an overview of backpropagation. PhD student Colin Raffel authored this collection of deep learning tutorials using Theano. It also seems to tackle a few Kaggle competitions, so you get a little bit of a lot with this one.Ī collection of tutorials on neural networks, using Theano A number of the common libraries are covered, as well as shell programming and Linux command line basics, and at least one then-current paper was implemented using the Python stack. Slides, code, and other information relating to the Fall 2013 Meetupsįrom UC Boulder's Research Computing group, this older collection of notebooks (it's from way back in Fall 2013) covers a wide range of material, with an apparent focus on Linux command line-powered data management. Bishop's "Pattern Recognition And Machine Learning." The notebooks of this simply-titled repository draw inspiration from Andrew Ng's Machine Learning course (Stanford, Coursera), Tom Mitchell's course (Carnegie Mellon), and Christopher M.
Start inotebook series#
Python coded examples and documentation of machine learning algorithmsĪaron Masino has shared a series of very detailed, very technical machine learning IPython Notebook learning resources. The material is likely best-suited to a beginner in machine learning, or someone with some understanding looking to master Scikit-learn. Validation, Density Estimation with Gaussian Mixture Models, and Dimensionality Reduction with PCA are a few of the more interesting topics covered you also get standards like k-Means, Regression, and Classification, don't worry. This repository, by Jake VanderPlas, is aimed at teaching Scikit-learn in the context of several different machine learning algorithms. These are seemingly non-nonsense tutorials, though likely useful mostly for the newcomer. This is a collection of notebooks and datasets, primarily put together by Nitin Borwankar, covering 4 algorithmic topics: Linear Regression, Logistic Regression, Random Forests, and k-Means Clustering. Open Content for self-directed learning in data science However, you would be well-advised to splurge and grab a copy of your own to fully understand the contents of the repo, and to fully embrace machine learning in the Python ecosystem. The repository is also fantastic, and a great resource unto itself. I don't vouch for many materials, but I highly recommend this book.
Start inotebook code#
This is the accompanying code for the fantastic book Machine Learning with Python, by Sebastian Raschka. The "Python Machine Learning" book code repository and info resource It also gets you focused on telling stories with data. It's a single notebook, but it's a good notebook to start with, as it whets your appetite for all tools analytic, including visualization. This warmup notebook is from postdoctoral researcher Randal Olson, who uses the common Python ecosystem data analysis/machine learning/data science stack to work with the Iris dataset. Repository of teaching materials, code, and data for my data analysis and machine learning projects
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