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Training Material | Addfor S.p.A.

The following IPython Notebooks are the standard training material distributed with the Addfor trainings. For more information about standard and custom training solutions please visit Training @ Addfor.

All the IPython notebooks are distributed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Installation instructions

We recommend to install the Anaconda distribution to the latest version: please visit to download Anaconda. The tutorials work with python3 (python2 is no longer supported). After Anaconda installation update the distribution to the latest release: conda update anaconda.

Clone this repository with git; use this command: git clone --depth 1 if you want to download only the current commit (faster, takes less disk space):

Create a shallow clone with a history truncated to the specified number of commits.

NOTE: for Windows users, you can use this git client, or choose to download: click Clone or download and then Download ZIP (in this case skip the git clone step).

Next cd into tutorials and create the environment addfor_tutorials from the file addfor_tutorials.yml (make sure the file is in your directory). Issue the command conda env create -f addfor_tutorials.yml (the process could take few minutes). After the installation is finished, activate the environment:

Windows: activate myenv macOS and Linux: source activate myenv

All notebooks use our Addutils library: please install Addutils (for python3) before running the Notebooks. Download the zip file and open the Terminal or Anaconda Prompt: source activate addfor_tutorials if environment is not already active, then type pip install (it should work for python3.4+).

At this point you are able to run the notebook with: jupyter-notebook and navigate through the directory tree.

Note: the first time you run the notebooks you could experience a brief slowdown due to matplotlib building its font cache. It should disappear the next session.

For more informations visit: Download training material guidelines @ Addfor


  1. Python + IPython/Jupyter
    1. An introduction to the IPython notebook
    2. Python Basic Concepts
    3. Python Getting Started
    4. Python Style Guide
    5. Python More Examples
    6. Object Oriented Programming in Python
    7. Integration of Python with compiled languages
    8. Unicode
    9. Regular Expressions
  2. NumPy
    1. Numpy Basic Concepts
    2. PyTables
    3. Numpy - Plotting with Matplotlib
    4. Scipy - Optimization
    5. Scipy Signal Processing: IIR Filter Design
    6. Symbolic Computation
  3. Pandas
    1. pandas Dataframe - Basic Operativity
    2. pandas I/O tools and examples
    3. Pandas Time series
    4. Statistical tools
    5. Merge and pivot
    6. Split apply and combine
    7. Sources of Open Data
    8. Baby Names
  4. Machine learning
    1. Definitions and Advices
    2. Prepare the Data
    3. The scikit-learn interface
    4. Visualizing the Data
    5. Dealing with Bias and Variance
    6. Ensemble Methods
    7. Ensemble Methods Advanced
    8. Support vector machines (SVMs)
    9. Predict Temporal Series
    10. Forecasting with LSTM
    11. Prognostics using Autoencoder
    12. Theano Basic Concepts
    13. Explore Neural Network Hyperparameters with Theano and Keras
    14. Neural Networks with Nervana Neon library
    15. Tensorflow Basic concepts
    16. Explore Neural Network Hyperparameters with TensorFlow
    17. TensorFlow for beginners
    18. Keras - Theano Benchmark
    19. Neon Benchmark
    20. TensorFlow Benchmark
    21. Neural Network Benchmark Summary