Get Started

Here are some useful resources to get started in Machine Learning

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

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. We recommend using at least Python 3.11.
For an IDE 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

Online Courses

These are some introductory online courses to get you started

AI For Everyone
- The “AI for Everyone” course on Coursera is a non-technical program that educates participants about AI, its potential applications, and its societal impacts, helping non-technical professionals understand and strategize around AI technologies.
Machine Learning Specialization
- The Machine Learning Specialization on Coursera, crafted by Andrew Ng, is a foundational program for beginners that teaches foundational AI concepts through a visual approach, followed by coding and mathematics. It’s expanded into three courses and is designed to be approachable for those with no prior math or rigorous coding background, making core curriculum more accessible.
Hugging Face Natural Language Processing Course
- Hugging Face’s NLP course is designed to provide a comprehensive understanding of natural language processing using the latest tools and techniques. It’s geared towards practitioners looking to apply NLP in real-world scenarios, offering hands-on experience with cutting-edge models and libraries.

Datasets

These are some free datasets to use

Google Dataset Search
From hands-on ML by Geron:
- Open Repos
- Meta portals
- Others
Wikipedia main one

University of Alberta Courses

Course Name	Course Number
Introduction to Data Science	CMPUT 191
Introduction to Principles and Techniques of Data Science	CMPUT 195
Ethics of Data Science and Artificial Intelligence	CMPUT 200
Game Artificial Intelligence	CMPUT 256
Introduction to Artificial Intelligence	CMPUT 261
Basics of Machine Learning	CMPUT 267
Games, Puzzles, Algorithms	CMPUT 355
Introduction to Reinforcement Learning	CMPUT 365
Search and Planning in Artificial Intelligence	CMPUT 366
Intermediate Machine Learning	CMPUT 367
Search, Knowledge and Simulation	CMPUT 455
Machine Learning	CMPUT 466
Artificial Intelligence Capstone	CMPUT 469
Probabilistic Graphical Models	CMPUT 463
Introduction to Natural Language Processing	CMPUT 461
Visual Recognition	CMPUT 328
Computer Vision	CMPUT 428
Ethics and Artificial Intelligence	PHIL 385
Statistical Methods for Learning and Data Mining	STAT 441
Visual Recognition	CMPUT 328

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.

Neural Networks: Zero to Hero with Andrej Karpathy A comprehensive educational resource that guides viewers from the basics of neural networks to more advanced concepts, showcasing practical examples and intuitive explanations. The series is known for its clear, engaging teaching style, making complex topics accessible to beginners and those looking to deepen their understanding of deep learning.

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.

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