- 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).
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...