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https://github.com/glemaitre/summer_school_2018


https://github.com/glemaitre/summer_school_2018

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README 

        
Introduction to Python and Machine Learning

===========================================



Instructors

-----------



- [Guillaume Lemaitre](https://glemaitre.github.io/)  [@glemaitre](https://github.com/glemaitre) - Inria, Université Paris-Saclay

- Mojdeh Rastgoo [@mrastgoo](https://github.com/mrastgoo) - Saint-Gobain Recherche



---



This repository will contain the teaching material and other info associated

with our python introduction and scikit-learn tutorial at the summer school

held from August 1-4 in University of Guadanarama and August 6-9 in Petronas

University.



The repository will be divided into the following subsections:



* `00_python` gives a general introduction to the Python programming language.

* `01_numpy` and `02_pandas` give a general introduction to numerical data

  processing using `numpy` and `pandas`.

* `03_sklearn` contains an in-depth scikit-learn tutorial.



Obtaining the Tutorial Material

--------------------------------



If you have a GitHub account, it is probably most convenient if you clone or

fork the GitHub repository. You can clone the repository by running:



```bash

git clone https://github.com/glemaitre/summer_school_2018.git

```



If you are not familiar with git or don’t have an

GitHub account, you can download the repository as a .zip file by heading over

to the GitHub repository (https://github.com/glemaitre/summer_school_2018) in

your browser and click the green “Download” button in the upper right.



![](images/download-repo.png)



Please note that we may add and improve the material until shortly before the

tutorial session, and we recommend you to update your copy of the materials one

day before the tutorials. If you have an GitHub account and cloned the

repository via GitHub, you can sync your existing local repository with:



```bash

git pull origin master

```



If you don’t have a GitHub account, you may have to re-download the .zip

archive from GitHub.



Installation Notes

------------------



This tutorial will require recent installations of



- [NumPy](http://www.numpy.org)

- [SciPy](http://www.scipy.org)

- [matplotlib](http://matplotlib.org)

- [pandas](http://pandas.pydata.org)

- [pillow](https://python-pillow.org)

- [scikit-learn](http://scikit-learn.org/stable/)

- [IPython](http://ipython.readthedocs.org/en/stable/)

- [Jupyter Notebook](http://jupyter.org)



The last one is important and you should be able to type:



    jupyter notebook



in your terminal window and see the notebook panel load in your web browser.

Try opening and running a notebook from the material to see check that it works. Alternatively you can use Jupyter lab.



For users who do not yet have the required packages installed, a relatively

painless way to install all the requirements is to use a Python distribution

such as [Anaconda](https://www.anaconda.com/download/ "Anaconda"), which includes

the most relevant Python packages for science, math, engineering, and

data analysis; Anaconda can be downloaded and installed for free

including commercial use and redistribution.

The code examples in this tutorial should be compatible to Python 2.7,

Python 3.4-3.6.



After obtaining the material, we **strongly recommend** you to open and execute

the Jupyter Notebook `jupter notebook check_env.ipynb` that is located at the

top level of this repository. Inside the repository, you can open the notebook

by executing



```bash

jupyter notebook check_env.ipynb

```



inside this repository. Inside the Notebook, you can run the code cell by

clicking on the "Run Cells" button as illustrated in the figure below:



![](images/check_env-1.png)



Finally, if your environment satisfies the requirements for the tutorials, the

executed code cell will produce an output message as shown below:



![](images/check_env-2.png)



Although not required, we also recommend you to update the scikit-learn the latest release version to ensure best compatibility with the teaching material. Please upgrade already installed packages by executing



- `pip install --no-deps --upgrade [package-name]`  

- or `conda update [package-name]`



Depending on how you installed ``scikit-learn``.



Data Downloads

--------------



The data for this tutorial is not included in the repository.  We will be

using several data sets during the tutorial: most are built-in to

scikit-learn, which

includes code that automatically downloads and caches these

data.



**Because the wireless network

at conferences can often be spotty, it would be a good idea to download these

data sets before arriving at the conference.

Please run**

```bash

python fetch_data.py

```

**to download all necessary data beforehand.**



The download size of the data files are approx. 280 MB, and after `fetch_data.py`

extracted the data on your disk, the ./notebook/dataset folder will take 480 MB

of your local hard drive.



Outline

=======



Introduction to Python

----------------------



- Introduction to Python programming language [[view](00_python/0_python.ipynb)]

- Introduction to NumPy (numerical data package) [[view](01_numpy/notebook.ipynb)]

- Introduction to Pandas (tabular data package) [[view](02_pandas/notebook.ipynb)]



Scikit-learn tutorial

---------------------



The full list of the tutorials is:



- 01 Introduction to machine learning with sample applications, Supervised and Unsupervised learning [[view](03_sklearn/notebooks/01.Introduction_to_Machine_Learning.ipynb)]

- 02 Scientific Computing Tools for Python: NumPy, SciPy, and matplotlib [[view](03_sklearn/notebooks/02.Scientific_Computing_Tools_in_Python.ipynb)]

- 03 Data formats, preparation, and representation [[view](03_sklearn/notebooks/03.Data_Representation_for_Machine_Learning.ipynb)]

- 04 Supervised learning: Training and test data [[view](03_sklearn/notebooks/04.Training_and_Testing_Data.ipynb)]

- 05 Supervised learning: Estimators for classification [[view](03_sklearn/notebooks/05.Supervised_Learning-Classification.ipynb)]

- 06 Supervised learning: Estimators for regression analysis [[view](03_sklearn/notebooks/06.Supervised_Learning-Regression.ipynb)]

- 07 Unsupervised learning: Unsupervised Transformers [[view](03_sklearn/notebooks/07.Unsupervised_Learning-Transformations_and_Dimensionality_Reduction.ipynb)]

- 08 Unsupervised learning: Clustering [[view](03_sklearn/notebooks/08.Unsupervised_Learning-Clustering.ipynb)]

- 09 The scikit-learn estimator interface [[view](03_sklearn/notebooks/09.Review_of_Scikit-learn_API.ipynb)]

- 10 Preparing a real-world dataset (titanic) [[view](03_sklearn/notebooks/10.Case_Study-Titanic_Survival.ipynb)]

- 11 Working with text data via the bag-of-words model [[view](03_sklearn/notebooks/11.Text_Feature_Extraction.ipynb)]

- 12 Application: IMDb Movie Review Sentiment Analysis [[view](03_sklearn/notebooks/12.Case_Study-SMS_Spam_Detection.ipynb)]

- 13 Cross-Validation [[view](03_sklearn/notebooks/13.Cross_Validation.ipynb)]

- 14 Model complexity and grid search for adjusting hyperparameters [[view](03_sklearn/notebooks/14.Model_Complexity_and_GridSearchCV.ipynb)]

- 15 Scikit-learn Pipelines [[view](03_sklearn/notebooks/15.Pipelining_Estimators.ipynb)]

- 16 Supervised learning: Performance metrics for classification [[view](03_sklearn/notebooks/16.Performance_metrics_and_Model_Evaluation.ipynb)]

- 17 Supervised learning: Linear Models [[view](03_sklearn/notebooks/17.In_Depth-Linear_Models.ipynb)]

- 18 Supervised learning: Decision trees and random forests, and ensemble methods [[view](03_sklearn/notebooks/18.In_Depth-Trees_and_Forests.ipynb)]

- 19 Supervised learning: feature selection [[view](03_sklearn/notebooks/19.Feature_Selection.ipynb)]

- 20 Unsupervised learning: Hierarchical and density-based clustering algorithms [[view](03_sklearn/notebooks/20.Unsupervised_learning-Hierarchical_and_density-based_clustering_algorithms.ipynb)]

- 21 Unsupervised learning: Non-linear dimensionality reduction [[view](03_sklearn/notebooks/21.Unsupervised_learning-Non-linear_dimensionality_reduction.ipynb)]

- 22 Unsupervised learning: Anomaly Detection [[view](03_sklearn/notebooks/22.Unsupervised_learning-anomaly_detection.ipynb)]

- 23 Supervised learning: Out-of-core learning [[view](03_sklearn/notebooks/23.Out-of-core_Learning_Large_Scale_Text_Classification.ipynb)]



For time constraint, we will most probably focus on the following list of tutorials: [01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 13, 14, 15, 17, 18, 19]. If time allows, we will cover the notebooks which are note specified.



References

----------



* The introduction to python notebook `00_python` is orginally presented in the [SciPy lecture notes](https://www.scipy-lectures.org/intro/language/python_language.html)

* The NumPy introduction `01_numpy` was originally presetned in the [Python workshop by the CDS](https://github.com/paris-saclay-cds/python-workshop/tree/master/Day_1_Scientific_Python) by Bartosz Telenczuk.

* The Pandas introduction `02_pandas` was originally presetned in the [Python workshop by the CDS](https://github.com/paris-saclay-cds/python-workshop/tree/master/Day_1_Scientific_Python) by Joris Van den Bossche.

* The scikit-learn introduction `03_sklearn` was originally prepared by Andy Mueller at the [SciPy conference](https://github.com/amueller/scipy-2018-sklearn)



.. [1] Andreas Mueller, "Introduction to Machine Learning with Python", 2017

   http://shop.oreilly.com/product/0636920030515.do