My Self-Created Artificial Intelligence Path
26 May 2020Contents
- Summary
- Python Programming Fundamentals
- Mathematics, Statistics and Probabilities
- Best MOOCs to Break Into AI
- Books
- Extras
Summary
The path of learning about Artificial Intelligence is often overwhelming with complex math and technical topics. Over the last year, I’ve been studying full time to break into AI along my master’s degree in Business Intelligence at HEC Montreal. Throughout my journey, I’ve collected an incredible amount of ressources and decided to showcase only ones that I found relevant. Let’s get started!
Key
- ✅ = course fully completed
- ✳️ = course partially completed or in progress
- ❎ = course not started yet
- No symbols for books and YouTube Channel
Python Programming Fundamentals
Basic programming knowledge is essential to succeed in data science. Since I was going to spend a lot of time learning new things, I also took a course to become an efficient learner.
- ✅ Learning to Learn [Efficient Learning]: Zero to Mastery (Source: Udemy)
- ✅ Python Pragramming Skill Track (Source: DataCamp)
- ✳️ Complete Python Bootcamp: Go from zero to hero in Python 3 (Source: Udemy)
- ✳️ Automate the Boring Stuff with Python Programming (Source: Udemy)
- Corey Schafer Youtube Channel
- Sentdex Youtube Channel
Mathematics, Statistics and Probabilities
Nowadays, thanks to various frameworks and libraries, much of the math work is done behind the scenes. However, having an intuitive understanding of the math helped me immensely with lots of underlying concepts. Due to my background in economics, I had already some mathematical prerequisite.
- ✅ Khan Academy Introduction to Matrices
- ✅ Khan Academy Statistics and probability
- ✳️ Mathematics for Machine Learning Specialization (Source: Coursera)
- 3Blue1Brown YouTube Channel
- StatQuest with Josh Starmer YouTube Channel
Best MOOCs to Break Into AI
Everyone who gets going in Machine Learning and Deep Learning gets overwhelmed by the plethora of MOOCs available online. I’ve spent a lot of time looking for the best AI courses. From my searches, these are packed with cutting-edge knowledge taught by the best practitioners in the field. Indeed, if you combine the theoretical teaching style of Andrew Ng and the top-down approach of Jeremy Howard, you definitely have best of both worlds.
- ✳️ Python for Data Science and Machine Learning Bootcamp (Source: Udemy)
- ✳️ Machine Learning by Stanford University (Source: Coursera)
- ✳️ Introduction to Machine Learning for Coders (Source: Fast.ai)
- ✅ Deep Learning Specialization (Source: Coursera)
- ✅ PyTorch for Deep Learning with Python Bootcamp (Source: Udemy)
- ❎ Practical Deep Learning for Coders (Source: Fast.ai)
- ❎ Part 2: Deep Learning from the Foundations (Source: Fast.ai)
Books
Sometimes a more traditional route is needed rather than always being in front of a screen. These books are incredible resources when one start out.
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Fluent Python by Luciano Ramalho
- Deep Learning for Coders with fastai and PyTorch by Jeremy Howard, Sylvain Gugger
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Extras
These resources below are for those who want to go further and stay up-to-date with the best AI practices.