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#### Jupyter Notebooks
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We will be using Jupyter notebooks (formerly ipython notebooks), which enable us to share with you the details of the python code of the week. The notebook environment provides the full story of the machine learning project. We see in the lecture that a ML project consists of multiple phases, starting from the analysis of the physical problem to the deployment of the model. The notebooks provide the mean to explain all these steps with visuals and share them with you in a convenient way.
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Jupyter notebooks are python files that can be run in a web browser. It also supports now multiple languages such including Java, R, Julia, Matlab, Octave, Scheme, Processing and Scala. The notebook itself is organized in the form of cells, which can be executed individually. The results of the code within the cell will be displayed right below that cell. It is also possible to run the cells independently, so that we can recreate cells, play with the order of cells and explore options independently. All the outputs will be embedded to the notebook as well –hence can be shared directly without even running the script. Another advantage is the publishing: GIT environments have Jupyter notebook rendering engines and all the work including the output can be shared with third parties explicitly. Combined with cloud combining, we can store the notebooks on the cloud, run the script remotely, save and share everything online, beginning to the end.
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#### <span dir="">Why are we using NumPy and Pandas?</span>
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