Here you will get an introduction to Material Science and Machine Learning using Python.
These examples will most likely only work with python 3.6+
Machine Learning while there are many algorithms and techniques the workflow is always the same.
While a huge generalization.
I feel that scientists fall mostly in the
Occam's razor: one should select the simplest model that describes the data.
As an example https://www.youtube.com/watch?v=1A1yaWS8gSg
Sofisticated model that predicts planet positions with circles can be replaced by a far simpler one that uses elispses. This comes from our understanding of the physics.
There are hundreds of algorithms to choose from. Always start with the simplest so that you can just how more complex models perform.
General Fields of Machine Learning. You will notice that some algorithms appear in multiple areas.
SVM, nearest neighbors, random forests, gradient boost, nearual networks.
Great starting example dataset
SRV, ridge regression, Lasso, Bayession Methods, neural networks
I would like to highlight how awesome bayession methods are. pymc3 is the python package to use. If you can create a model that describes your data you can use bayessian methods. It not gaussian processes are amazing (they are "parameter free" fitting methods.
Gaussian process. Notice how you get the variance of your prediction with your data.
Bayessian Methods predicting the effect of regulation on coal miner deaths.
I am not very knowledgable on neural networks but pytorch is the most userfriendly way to get started.
Play with neural networks in your browser to get a feel for them. link
k-Means, spectral clustering, mean-shift
Cross Validation): split your data into a traning and test set. Use the training set to fit your model. Use the test set to evaluate the performance of your model.
Often times you split your data 90% training, 10% testing.
sklearn provides many methods for automating this.