Add web site contents

Author: gjbex

docs/README.md

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+The Python programming language is increasingly popular.  It is a
+versatile language for general purpose programming and accessible
+for novice programmers.  However, it is also the de facto go-to
+language for machine learning applciations.  This training
+introduces modules that are useful in that context.
+
+
+## Learning outcomes
+
+When you complete this training you will
+
+  * understand what supervised and unsupervised learning are;
+  * understand the workflow required for supervised learning;
+  * be able to implement that workflow in scikit-learn;
+  * know how to use algorithms in scikit-learn such as ridge regression
+    and naive Bayes classification;
+  * be able to apply k-means clustering;
+  * understand the main concepts in deep neural networks;
+  * be able to apply a convolutional neural network (CNN) to an image
+    classification task;
+  * understand the concepts underpinning word embeddings;
+  * be able to use recurrent network architectures such as 
+    long short term memory (LSTM) to natural language problems;
+  * be aware of best practices and pitfalls in machine learning.
+
+
+## Schedule
+
+Total duration: 4 hours.
+
+  | Subject                                     | Duration |
+  |---------------------------------------------|----------|
+  | introduction and motivation                 | 20 min.  |
+  | scikit-learn: regression                    | 40 min.  |
+  | scikit-learn: classification                | 30 min.  |
+  | science-learn: clustering                   | 20 min.  |
+  | coffee break                                | 10 min.  |
+  | introduction to neural networks             | 10 min.  |
+  | Keras: image classification with CNNs       | 40 min.  |
+  | Keras: sentiment classification with LSTM   | 40 min.  |
+  | hyperparameter optimization                 | 30 min.  |
+  | wrap up                                     | 10 min.  |
+
+
+## Training materials
+
+Slides are available in the
+ [GitHub repository](https://github.com/gjbex/Python-for-machine-learning),
+as well as example code and hands-on material.
+
+
+## Target audience
+
+This training is for you if you need to use Python for machine learning
+pplcations.
+
+
+## Prerequisites
+
+You will need experience programming in Python.  This is not a training that starts
+from scratch.  Familiarity with numpy is not required, but would be beneficial.
+Familiarity with numpy, pandas and matplotlib is strongly recommended.
+
+If you plan to do Python programming in a Linux or HPC environment you should
+be familiar with these as well.
+
+
+## Trainer(s)
+
+  * Geert Jan Bex ([[email protected]](mailto:[email protected]))