A non-techie’s path to learning AI and ML
The noise cancellation technology that Google Meet recently introduced or the speech recognition technology in Alexa that keeps getting better with use — have always left me wondering about the powerful technology that makes all of this possible. And that’s how I started developing an interest in Machine Learning (ML) and Artificial Intelligence (AI).
Being a tech writer and content curator from a non-engineering background but a strong passion for technology, I set out to explore ways in which a non-coder, layman like me can comprehend how these life-changing technologies work. In the interest of anyone who wants to understand the nuances of this technology but cannot read/write a line of code/statistics, I’ve put together a list of some good starters that helped me warm up to the bare concepts of AI and ML.
Because of the traction that these technologies have gained over the past few years, the resources available on the internet can be quite overwhelming. Learning platforms like Coursera and LinkedIn Learning have thousands of ML and AI course offerings but most of these courses are designed for data scientists who need to learn how to train models and develop algorithms for ML.
That being said, I’d strongly recommend taking only the introduction lesson of the famous Andrew Ng’s Machine Learning course on Coursera to wrap your head around the fundamentals of ML and the broad categories that most algorithms fall into. The lecture doesn’t last more than an hour and it’s free!. The rest of the course is aimed at data scientists and dives deeper into using linear algebra and statistics to create different types of ML models.
Next up, I spent some time researching articles and blogs that could break down the concepts of ML without getting too technically abstruse. I found this awesome blog series that’s so true to its name — Machine Learning is Fun! The author has written this blog in 8 parts for curious-heads like me who don’t know where to start. He’s made the whole exercise of learning fun with the help of relatable examples that simplify the concepts of supervised/unsupervised learning and the different algorithms that fall in its purview. Though the concepts become increasingly complex with each post, the understanding of the intense concepts feels seamless.
To further my knowledge a bit more about how AI and ML fit in together, I took this conceptual course on Udemy — Machine Learning for Absolute Beginners — Level 1. You can enter this course with a clean slate and emerge knowing enough to fuel your enthusiasm for this mind-shifting technology. If you’re interested in pursuing the practical side of data science without any prior knowledge of the technology, you can take the next course in this Absolute Beginners series that walks you through the basics of Python (the most frequently used programming language for data science) and Pandas (the Python library used for manipulating data structures).
Learnings and courses aside, I found it extremely essential to keep myself abreast of the latest trends and news about ML and AI around the world. I found some free blogs and newsfeeds that you can subscribe to and receive your daily dose of the most recent and emerging ML trends. There are plenty out there, but these are the ones I’m subscribed to and find insightful -aitrends.com, sciencedaily.com, the Google AI blog, becominghuman.ai, and towardsdatascience. Note: This list is not exhaustive at all. Please feel free to point out other such resources that discuss the latest trends and developments in the world of AI and ML.
Finally, I’d recommend soaking up any free learning resources for beginners that are available to you in the form of books, webinars, discussions, or more. Thanks to a free hands-on webinar workshop that my organization recently conducted for absolute ML beginners, I wrote my first-ever Machine Learning program that predicts the price of a house based on the area of the house. It was a fun learning experience and I’ll write more about it in my next blog post.
I hope you found this post useful.
I read this somewhere and it stayed with me ‘Machine Learning is more about Maths than about Python or any libraries.’. Programmer or not, ML is easily comprehensible to minds with logical reasoning aptitude and maths skills.
