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data-science

Notebooks and Python about data science

Learning data science step by step

Most of the examples presented in Internet tutorials are either using powerful libraries (Scikit Learn, Keras…), complex models (neural nets), or based on data samples with many features.

In this collection of workbooks, I want to start from simple examples and raw Python code and then progressively complexify the data sets and use more complex technics and libraries.

On purpose, most datasets are generated in order to adjust the parameters fitting with the demonstration.

The notebooks are of type Jupyter, using Python 3.7

To read or edit the notebooks you may :

Do not get confused

Linear regression

Let’s progressively start from simple univariate example and then add progressively more complexity:

Classification

Binary classification with parametric models

Binary classification with non-parametric models

Multi-class classification with regression or neural networks

Multi-class classification with non-parametric models

Deep learning

Convolutional neural networks (CNN)

Generative networks (VAE, GAN)

Natural Language Processing (NLP)

Reading list

Books

Nice notebooks

Tutorials and courses

Papers

Data / model sources

Word embeddings & analysis