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I wrote a blog post exactly on that: http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph... (including the data science part).

I strongly advice for:

- using Python in the interactive environment Jupyter Notebook,

- starting with classical machine learning (scikit-learn), NOT from deep learning; first learn logistic regression (a prerequisite for any neural network), kNN, PCA, Random Forest, t-SNE; concepts like log-loss and (cross-)validation,

- playing with real data,

- it is cool to add neural networks afterwards (here bare TensorFlow is a good choice, but I would suggest Keras).

Links:

- http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

- http://hangtwenty.github.io/dive-into-machine-learning/

- https://github.com/leriomaggio/deep-learning-keras-euroscipy...




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