Kanber - Hands-on machine learning with JavaScript: solve complex computational web problems using machine learning

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Burak Kanber is an entrepreneur and engineer with nearly 20 years of experience developing software. After completing graduate school at The Cooper Union, Burak joined a marketing technology startup as co-founder and CTO. Machine learning was not as accessible as it is today, so from 20102012 he wrote a series of articles that taught ML to web developers from first principles. His ML in JS series has since been enjoyed by over a million readers.

Burak is also the co-author of Genetic Algorithms in Java Basics , published by Apress in 2015.

Paulo Pires is a geographical engineer who, in his early thirties, decided to apply his skills in business intelligence. He started working in Webdetails seven years ago and specialized in frontend dashboards with CTools, Pentaho, HTML, JavaScript and its libraries, CSS, and some backend ETL. Currently, he is a senior consultant.

As a hobby, he likes to explore new technologies, and lately he has been playing with Home Automation with Node.js, Sonoff sensors, and Mosquitto broker.

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I started my first deep dive into machine learning (ML) back in 2008 when I was developing algorithms for adaptive traction control systems for electric cars. Shortly thereafter, I moved out of the mechanical engineering field and co-founded a marketing technology startup. Within a few weeks, I realized how important ML was going to be for me and my company, and I decided to read every book and paper on ML that I could find.

I spent the next couple of years with my nose to the grindstone, reading dozens of textbooks and hundreds of academic papers, writing every algorithm I could find from scratch, and slowly developing an intuition and philosophy around ML.

During that period, I discovered a few things about the ML ecosystem that I wasn't too happy with. There was a strong culture of gatekeeping at the time. The idea of writing ML in a language other than Python was considered absurd. There was a sentiment that only people who had gone to school to study ML could possibly be successful in the field. Much of the publicly available reading material, such as blog articles and tutorials online, was written in a distinctly mathematical tone, thereby alienating readers who were unfamiliar with linear algebra and vector calculus.

At the same time, I was teaching a JavaScript (JS) programming bootcamp and many of my studentsself-taught web developersexpressed an interest in ML. It was difficult for me to point them in the right direction; their only option at the time was to move to Python.

This was frustrating to me because I knew that my students were smart enough to grasp ML, and I also knew that ML didn't need to be confined to Python. I also got the sense that many of the developers using the popular Python libraries didn't actually understand the mechanism of the algorithms and would run into problems when trying to implement them. The gatekeeping had backfired and only served to reduce this powerful family of algorithms into black boxes that were applied carelessly by developers that were discouraged from digging deeper and learning more.