Machine Learning or ML is fast becoming the buzzword of our time, but why are so many developers falling short when it comes to getting their heads round this essential skill? Here’s why developers must tackle ML to get ahead and what’s standing in their way.
Let’s face it, when it comes to AI, the future has very much arrived. This application of ML is everywhere right now, whether you’re looking at self-driving cars or self-tuning database systems – it’s impacting almost every industry on the market. Acquiring ML skills is a no-brainer for the ambitious developer, and the number of self-led courses and MOOCs doubled last year. However, despite the urgency to get ML under their belts, developers are struggling to master the key skills that ML demands.
Forbes nicely rounds up the challenges developers need to overcome…
Firstly, there’s the undeniable link to mathematics – an intimidating prospect for those to whom skill with numbers doesn’t come naturally. If linear algebra, probability and statistics make you nervous, it’s time to brush up – get the foundations in place to master ML and you’ll make your life a whole lot easier.
Some developers may also be put off by the data analysis side of ML. Even if you don’t eat, breathe and sleep histograms, bar charts and the like, you need to practice visualisation. Microsoft Excel can help with developing your understanding of Pivot Tables, charts etc.
Another obstacle is the mind-bending battle of Python vs. R vs. Julia – the debate continues as to which is best for developing ML models. Each church has its own devoted disciples, but if you’re looking to start educating yourself on one of them from scratch, you may end up conflicted by different arguments.
Forbes recommends starting with Python – it has an excellent data science ecosystem and seems to be edging out in front of the competition. It also works nicely with frameworks such as Scikit-Learn, Caffe2 and Keras to start your ML adventure on the right foot.
Developers also struggle with the fact ML offers multiple techniques to solve the same problem – it can feel like there are too many algorithms to choose from. The best strategy is to learn the core concepts attached to different algorithms – it will make it easier to choose which is best.
Once a developer has made the decision to throw everything they’ve got at these challenges and educate themselves on the ins and outs of ML, they’ll also face the fact that there’s a bewildering amount of open online courses to choose from. Bear in mind that the tools and frameworks associated with ML are in a constant state of evolution and none of these courses can provide a complete understanding of a topic so vast.
However, instead of referring to too many guides for information and getting bogged down by conflicting content, choose a single course per concept. Slow but steady wins the race.
I would encourage ambitious developers to embrace ML: “Machine Learning has become a huge trend in the tech sector over the last few years. As we see more real-world impacts from these technologies, ML might even prove to be one of the most critical skills of our time. Developers who can master this tricky area can expect to open more doors and reap greater financial rewards as we move into 2018 and beyond.”
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