How should web developers learn machine learning?

Do you have the motivation to learn machine learning? Given its relevance in today's landscape, you should be motivated to learn about this field. But if you're a web developer, how do you go about learning it? In this article, I show you how. So, let’s break this down.

What is machine learning?

You may be wondering why machine learning matters to you, or how you would even go about learning it. Machine learning is a smart way to create software that finds patterns in data without having to explicitly program for each condition. Sounds too good to be true? Well it is. Quite frankly many of the state-of-the-art solutions to the toughest machine learning problems don’t even come close to reaching 100 percent accuracy and precision. This might not sound right to you if you’ve been trained, or have learned, to be precise and deterministic with the solutions you provide to the web applications you’ve worked on. In fact, machine learning is such a challenging problem domain that data scientists describe problems to be tractable or not.

Computer algorithms can solve tractable problems in a reasonable amount of time with a reasonable amount of resources, whereas, in-tractable problems simply can’t be solved. Decades more of R&D is needed at a deep theoretical level, to bring approaches and frameworks forward that will then take years to be applied and be useful to society.

Did I scare you off? Nope? Okay great. Then you accept this challenge to learn machine learning. 

But before we dive into how to learn machine learning, let's answer the question: Why does learning machine learning matter to you?  Well, you're a technologist and as a result, it’s your duty, your obligation, to be on the cutting edge. The technology world is moving at a fast clip and it’s accelerating. Take for example, the shortened duration between public accomplishments of machine learning versus top gaming experts. It took a while to get to the 2011 Watson v. Jeopardy champion, and far less time between AlphaGo and Libratus.

So what's the significance to you and your professional software engineering career?

Elementary dear my Watson—just like the so-called digital divide between non-technical and technical lay people, there is already the start of a technological divide between top systems engineers and the rest of the playing field in terms of making an impact and disrupting the way the world works.  Don’t believe me? When’s the last time you’ve programmed a self-driving car or a neural network that can guess your drawings?

Making an impact and how to learn machine learning

The toughest part about getting started with machine learning is figuring out what type of problem you have at hand because you run the risk of jumping to potential solutions too quickly before understanding the problem. Sure you can say this of any software design task, but this point can’t be stressed enough when thinking about how to get machines to recognize patterns in data. There are specific applications of machine learning algorithms that solve a very specific problem in a very specific way and it’s difficult to know how to solve a meta-problem if you haven’t studied the field from a conceptual standpoint. For me, a break through in learning machine learning came from taking Andrew Ng’s machine learning course on courser. So taking online courses can be a good way to start learning. 

If you don’t have the time, you can learn about machine learning through numbers and images. Let's take a look. 

Numbers

Conceptually speaking, predicting a pattern in a single variable based on a direct—otherwise known as a linear relationship with another piece of data—is probably the easiest machine learning problem and solution to understand and implement.  The following script predicts the amount of data that will be created based on fitting a sample data set to a linear regression model: https://github.com/ctava/linearregression-go.

Because there is somewhat of a fit of the sample data to a linear model, the machine learning program predicted that the data created in the fictitious Bob’s system will grow from 2017, 2018. 

Bob’s Data 2017: 4401
Bob’s Data 2018 Prediction: 5707 

This is great news for Bob and for you. You see, machine learning isn’t so tough after all. I’d like to encourage you to save data for a single variable—also known as feature—to a CSV file and see if you can find that the data has a linear relationship with time. The following website is handy in calculating the number of dates between two dates: https://www.timeanddate.com/date/duration.html. Be sure to choose your starting day and year appropriately at the top of the file to fit your data.

Images

Machine learning on images is exciting! It’s fun to see what the computer comes up with in terms of pattern recognition, or image recognition. Here’s an example using computer vision to detect that grumpy cat is actually a Persian cat: https://github.com/ctava/tensorflow-go-imagerecognition.

If setting up Tensorflow from source isn’t your thing, not to worry. Here’s a Docker image to start off with: https://github.com/ctava/tensorflow-go.

Once you’ve followed the instructions in the readme.md file, simply: 

• Get github.com/ctava/tensorflow-go-imagerecognition
• Run main.go -dir=./ -image=./grumpycat.jpg

Result: BEST MATCH: (66% likely) Persian cat

Sure there is a whole discussion on this topic alone in terms of what Tensorflow is, what’s a tensor, and what’s image recognition. But I just wanted to spark your interest so that maybe you’ll start to look at the amazing advances in the computer vision field.

Hopefully this has motivated you to learn more about machine learning based on reading about the recent advances in the field and seeing two simple examples of predicting numbers, and classifying images.I’d like to encourage you to keep up with data science in general.

About the Author 

Chris Tava is a Software Engineering / Product Leader with 20 years of experience delivering applications for B2C and B2B businesses. His specialties include: program strategy, product and project management, agile software engineering, resource management, recruiting, people development, business analysis, machine learning, ObjC / Swift, Golang, Python, Android, Java, and JavaScript.