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These Machine Learning projects were created during a Specialization at Holberton School. This repository contains different projects that cover maths, reinforcement, supervised, and unsupervised learning, data series analysis, transformers, aoutoencoders, and more.

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Machine Learning Specialization


Requirements

Python Scripts

  • Allowed editors: vi, vim, emacs
  • All your files will be interpreted/compiled on Ubuntu 16.04 LTS using python3 (version 3.5)
  • Your files will be executed with numpy (version 1.15)
  • All your files should end with a new line
  • The first line of all your files should be exactly #!/usr/bin/env python3
  • A README.md file, at the root of the folder of the project, is mandatory
  • Your code should follow pycodestyle (version 2.5)
  • All your modules should have documentation (python3 -c 'print(import("my_module").doc)')
  • All your classes should have documentation (python3 -c 'print(import("my_module").MyClass.doc)')
  • All your functions (inside and outside a class) should have documentation (python3 -c 'print(import("my_module").my_function.doc)' and python3 -c 'print(import("my_module").MyClass.my_function.doc)')
  • Unless otherwise noted, you are not allowed to import any module
  • All your files must be executable
  • The length of your files will be tested using wc

More Info

Installing Ubuntu 16.04 and Python 3.5

Follow the instructions listed in Using Vagrant on your personal computer, with the caveat that you should be using ubuntu/xenial64 instead of ubuntu/trusty64.

Python 3.5 comes pre-installed on Ubuntu 16.04. How convenient! You can confirm this with python3 -V Installing pip 19.1

wget https://bootstrap.pypa.io/get-pip.py
sudo python3 get-pip.py
rm get-pip.py

To check that pip has been successfully downloaded, use pip -V. Your output should look like:

$ pip -V
pip 19.1.1 from /usr/local/lib/python3.5/dist-packages/pip (python 3.5)

Installing numpy 1.15, scipy 1.3, and pycodestyle 2.5

$ pip install --user numpy==1.15
$ pip install --user scipy==1.3
$ pip install --user pycodestyle==2.5

To check that all have been successfully downloaded, use pip list. Tasks


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These Machine Learning projects were created during a Specialization at Holberton School. This repository contains different projects that cover maths, reinforcement, supervised, and unsupervised learning, data series analysis, transformers, aoutoencoders, and more.

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