Please install the latest stable version of:
If have you never installed SciPy on your laptop, it might be complicated to build it from the source (you need a fortran compiler and an optimized BLAS implementation such as Atlas). In that case you should instead download a binary distribution of Python and all of the above packages called Anaconda at:
Once installed check that you can start the ipython notebook with the command:
Then open a new notebook and in the first cell type (remove any leading whitespaces):
run the notebook cell by clicking on the run button of the notebook UI. You should get the version numbers of the installed packages.
If you get an error message please feel free to send the instructor an email at olivieratogrisel.com with the detailed error message, your operating systems version and the installation method you used.
Scikit-learn is a versatile Machine Learning library for Python that blends well with the NumPy and SciPy ecosystem and is used by a growing user-base of both academic researchers and data scientists and engineers in the tech industry.
IPython with its notebook interface is an interactive programming environment that is particularly well suited for data exploration, modelling and sharing of analysis results notably via nbviewer.ipython.org.
The objective of this tutorial is to get acquainted both with Machine Learning concepts in general and the pydata ecosystem in particular.
The session will cover the following topics:
- how to extract a Machine Learning friendly representation of raw data (feature extraction),
- how to train various machine learning models such as Logistic Regression, Support Vector Machines and randomized ensembles of decision trees,
- how to evaluate the predictive accuracy of a model and detect overfitting,
- how to automatically tune the model parameters from data.
Olivier Grisel is a software engineer in the Parietal team of INRIA. He works to improve the speed and scalability of the scikit-learn machine learning library for the Python / Numpy / Scipy ecosystem. He also likes to share interesting Machine Learning papers and tricks on twitter: @ogrisel
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