Get Started

Here are some useful resources to get started in Machine Learning

If you feel overwhelmed or have questions, ask on our slack!

• First, Download python. Before doing so, you can check to see if you already have Python installed by opening a Terminal (Mac or Linux) or Command Prompt (Windows) and typing python or python3. If a shell opens, note the version number. For Tensorflow to work effectively, we recommend using at least Python 3.6.7.
• Once you have Python installed, pip should be installed by default. Try the following commands to install OpenAI Gym: pip install gym. If this doesn’t work, try the variants: python -m pip install gym or py -m pip install gym or pip3 install gym. This will be dependent upon what command you type in the shell to make Python run.
• Once you have Gym installed, begin working through the Documentation tutorial. To get the code running, we recommend either using Wing101 Ide, Visual Studio Code, or Sublime Text Editor.
• For two more non-standard IDE’s, consider using Atom, Jupyter Notebook, or Vim. For the latter, type pip install jupyter [see variants above].
• If you want some fun challenging exercises in Python to start playing around, consider
  • GitHub Learning Lab
  • Code Academy
  • Learn Python Tutorials
  • Challenging exercise
• After this, we began working through this series of OpenAI Gym tutorials. We went through the first two steps of making the initial population.
• To apply neural networks, we have to then use Tensorflow. For the basics of Tensorflow:
  • Tutorials Page
  • Adventures in Machine Learning:
    • Tensorflow
    • Neural Networks
    • Deep Q Learning.
  • Keras Activation

Datasets

These are some free datasets to use

• Google Dataset Search
• From hands-on ML by Geron:
  • Open Repos
    • UC Irvine ML Repo
    • Kaggle datasets
    • AWS datasets
  • Meta portals
    • Data Portals
    • OpenDataMonitor
    • Quandl
  • Others
    • Wikipedia
    • Quora
    • Subreddit
• Wikipedia main one

University of Alberta Courses

Popular Lectures

General Machine Learning

Machine Learning with Andrew Ng, Stanford University Universally recommended machine learning lectures by Andrew Ng, for beginner and intermediate level machine learning.

Elements of AI Free online course made by the University of Helsinki and Reaktor.

Reinforcement Learning

Reinforcement Learning with David Silver Older but popular lecture series by lead DeepMind researcher David Silver at King’s College London. Follows along with the first edition of Introduction to RL by Sutton and Barto.

Deep Learning

Deep Learning and Reinforcement Learning Summer School 2018 These are the lecture videos from the 2018 DLRL summer school, held by CIFAR, Amii, and the Vector Institute in Toronto. The DLRL summer school is pretty competitive, but the lectures actually vary from introductory to advanced. Links to the slides are also included on the video pages.

Advanced Deep Learning and Reinforcement Learning with DeepMind A brand new lecture series (2018) done at UCL by a bunch of DeepMind staff.

Miscellaneous

Compiled r/MachineLearning Links This is a compilation of links created by the Machine Learning subreddit on reddit, containing various free resources

r/artificial