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Facebook messenger chat bots. Fraud detection. The creepy way that Netflix knows what you like. Self-driving cars. What do all of these technologies have in common? They’re all run by machine learning (ML).
IDC forecasts that machine learning and AI spending will increase from $12 billion in 2017 to $57.6 billion by 2021, while data science platforms that support machine learning are predicted to grow at a 13% (Compound Annual Growth Rate) through 2021. In short, it's a big deal, which is why there's a sudden rush for developers to learn relevant the tools and tech of the trade.
But it's not all Westworld extremism and Elon Musk. ML has practical applications that extend to almost every industry. We break down the top three industries affected by the coming of the robots below.
Simply put, it’s a branch of artificial intelligence science that aims to teach computers how to learn from data, without being specifically programmed to do so. These algorithmic models allow computers (or robots) to independently adapt to new data, search for patterns, and then learn from previous computations to produce reliable decisions and results.
There is a lot of data being produced in all areas of manufacturing. Most often, machine learning is implemented to quickly and accurately detect anomalies, allowing for much more efficient and low cost production.
Some technologies being manufactured are also starting to incorporate machine learning into the product itself. Self driving cars is the biggie we're all excited about. They’re run by machine learning in order to make split-second, risk-optimized decisions on the road.
Finance has long used machine learning in conjunction with data science. The sheer amount of data in this industry is much better organized, analyzed, and put to use by computers. When combined with data science, computers can process the data and use it to make investments or manage stock portfolios, without the bias that humans naturally have.
On top of that, a new development is machine learning being used to creatively expand the industry. It can help devise new business opportunities, deliver customer services, and even detect banking fraud.
Machine learning in these areas includes everything from personalized ads, curated timelines, recommended products, and chatbots with automated replies.
If you’ve ever come across one of these - nice! Your life has been helped (or maybe a little harassed?) by machine learning! There are thousands of ways machine learning can take consumer data and use it to provide better services and products. With the incorporation of video in the physical store environment, retailers will even be able to analyse customers as they walk in, and will be able to see which products people are looking at, and even where they are looking on the product – whether that’s the price, the features or the picture on the box. Of course, while this might help to save time, it has its dark side, with many hotly debating the ethical nature of increasingly intelligent the algorithms, and their ability to control our pockets and behavior.
No wonder that machine learning (and artificial intelligence!) are one of the fastest growing fields, with an almost extreme amount of demand for those who have studied it. Want to be a part of it?
According to a survey from Tech Pro Research, only 28% of companies have some experience with AI or Machine Learning, and more than 40% said their enterprise IT personnel don’t have the skills required to implement and support AI and/or Machine Learning. Below are some key skill areas that are required to work in the field:
Data Science – Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
Software Engineering – A Machine Learning Engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services. Some of the popular programming languages within Machine Learning in are Python, R, Java, and C++.
Probability – Most machine Learning algorithms are about dealing with uncertainty and making reliable predictions. The mathematical tools to deal with such settings are found in principles of probability and its derivative techniques.
Statistics – Also of importance are tools and techniques that enable the creation of models from data. Machine Learning algorithms are often built upon statistical models.
Whatever the field you want to go into, there’s no way that machine learning won’t be a core part of it - becoming an expert in the tech behind ML can only help you get there!