So you want to learn how to work with Artificial Intelligence. Maybe you have a problem you want to solve, maybe you want to grow your career prospects, or maybe you’re simply just a curious cat. Whatever your reason, this article will present you with a curated list of online resources for you to self-guide your Artificial Intelligence education for FREE. I will be referencing educators/educational resources for different parts of the learning phase, and organise it into different options to suit different learning approaches.
There are essentially 3 stages to learning how to develop Artificial Intelligence:
- Mathematics – especially topics under Linear Algebra, Calculus, Statistics and Probability
- Computer Programming Fundamentals – this is essentially developing your understanding of computational thinking, learning a suitable programming language and familiarising yourself with Computer Science fundamentals
- The last stage, is diving into the specific dimension of AI development.
CLARIFICATION: Artificial Intelligence vs Machine Learning
Simply put, Artificial Intelligence is the broader concept of machines/technology being able to carry out tasks in a way that we would consider “smart”. It is the field of discipline on human behaviour mimicry through machines. Machine Learning on the other hand, is a current application of AI and is based around the idea of machines accessing data and learning from it. BONUS- Deep Learning: this is an even narrower subset of Machine Learning that utilises certain ML tools and techniques, and applies them to solving problems that learns and makes decisions on its own – and hence mimicking human thought.
First of we start with Mathematics for Machine Learning by Siraj Raval – an online AI/Machine Learning educator (we’ll be referencing him a lot)
The objective of this video is to give you a clear idea of why mathematics is important for Machine Learning and how the different fields of mathematics previously mentioned are made use of.
- Reading: The Deep Learning Book – Part 1
- Essence of Linear Algebra – 15 videos
- Essence of Calculus – 12 videos
- Machine Learning Playlist by Geek’s Lesson – Videos 2, 3, 4 (note: you can watch videos 1 through 7 to get right into it)
- KhanAcademy – Statistics and Probability, Linear Algebra, Calculus 1, Calculus 2 & Multi-variable Calculus
Bonus Note: KhanAcademy is structured such that it also offers quizzes at the end of modules to soldify the learning. So this is both a video resource as well as an activity.
- Essential Maths for Machine Learning: Python Edition . Note: This requires basic Python knowledge, please refer to the next section and cover the fundamentals then return to this.
Computer Programming Fundamentals and Computational Thinking
This part of the learning phase is primarily focused on developing the learner’s skills in computer programming and computational thinking.
- Google’s Exploring Computational Thinking – Go through the course to get an overview of what computational thinking involves
- Learn Python – Codecademy, Learn Python, FreeCodeCamp.org, and then check out Python 3’s documentation
This segment of the learning phase refers to pre-curated resources available online for the learners, who are now equipped with both the mathematical know how and the computational capabilities, to focus their learning towards the AI field.
- Artificial Intelligence – by Columbia University
- Machine Learning – by Columbia University
- Siraj Raval’s Machine Learning Curriculum – Overview here
- Siraj Raval’s Deep Learning Curriculum – Overview here
- Machine Learning – Stanford
- Machine Learning Course by Google
- Deep Learning Book – Hint: Use the contents page as a curriculum to guide your learning through other material
- MIT Open Course Ware – Artificial Intelligence (Undergraduate)
Hopefully by working through the material referenced above and self-selecting the resources that best work for your learning style, you have developed the foundational step in your journey through AI development. More importantly, you have developed a wholesome perspective of the field at large.
Now, you are the sailor of your own learning boat, so what will you steer to next?