Machine learning has become a massive buzzword, a term often coupled with artificial intelligence and is almost always present in talks, conferences, and events centered on technological innovations. While it’s easy to get excited about the term and get eager to dabble with the technology, any aspiring machine learning developer should first be aware of what machine learning truly entails and how it’s a driving force for recent artificial intelligence breakthroughs.
Artificial intelligence has come a long way since its first formal inception in 1956, during which time the idea of thinking machines that could perform complex tasks requiring human cognition was but a theoretical construct. Early artificial intelligence systems, such as the widely successful expert systems, simulate the human decision-making process by being programmed with every correct decision for each specific situation. However, this required explicitly programming every possible scenario and the corresponding decision that the machine should take for each one. It quickly became evident that this approach would not be feasible for applications that had a large number of possible outcomes. For example, this proved impossible even for something as simple as a chess game, which has a total of 288 billion possible configurations after just the first four moves.
The emergence of machine learning was a groundbreaking moment in the field of artificial intelligence, as it paved the way for machines that did not have to be explicitly programmed, but instead are able to learn from experience just as a human would. It has since become the main approach towards creating artificially intelligent machines, mainly because a machine learning developer doesn’t have to know and program every correct decision the machine should make; instead, they only have to properly implement machine learning algorithms that allow the machine to learn by itself.
In this article, we will take a deeper look at the skills a machine learning developer should have and how they are being utilized in the Industry.
Machine learning developers
Due to the promising potential of machine learning to efficiently create artificially intelligent systems that possess the ability to learn, the demand for machine learning has soared. It has become the fastest growing job on LinkedIn, with 1,829 machine learning jobs listed on the site and an almost 10-fold increase in employed machine learning professionals today compared to five years ago. According to McKinsey’s December 2016 report, among the industries where machine learning is most demanded are retail, manufacturing, healthcare, the public sector, and location-based businesses. Moreover, the countries that offer the greatest salary to machine learning professionals are the United States, Switzerland, Norway, Australia, and Canada.
Machine learning in the industry
A great machine learning specialist must be fully aware of the technology landscape of his/her skills, including the market at which their skills are targeted. This allows them to learn about the best implementations for technologies that have already been executed, and it also enables them to contribute valuable suggestions and ideas to businesses instead of just blindly following instructions from higher-ups. To give us a better picture of the extent to which machine learning is being used to deliver top-notch services, here’s how the world’s leading companies utilize machine learning to stay at the forefront of technology.
Microsoft is the world’s leading software company, and unsurprisingly, it utilizes machine learning in many of the products it offers. Its virtual assistant, Cortana, serves as a personal companion for Windows, Android and iOS users that can understand human language and respond accordingly, and has gotten critical reception for its human-like conversations and quick response times. Windows Hello is Microsoft’s iris scanning feature in Windows 10, which authorizes and authenticates users by just scanning their iris, and is highly lauded for its accuracy, very quick authentication time, and ease of use. Microsoft’s search engine, Bing, also uses machine learning for various purposes, including its ability to predict winners in various competitions as well as in public elections. It rose to popularity for correctly predicting every single winner of the 2014 World Cup, beating all other predictive platforms.
Google is another leading tech company that utilizes machine learning in many of its products. It has its own virtual assistant, simply named Google Assistant, which can understand human speech just like Microsoft’s Cortana and is praised for its more casual, fun, and down-to-earth demeanor compared to other virtual assistants. Google Translate, which is arguably the most popular language translation tool available, also makes use of machine learning algorithms to accurately translate text from one language to any other language. Google Photos likewise makes use of machine learning to recognize objects in photos, so that you can search for any object in your album and Google Photos will show you a list of your photos that contain the said object. Google Photos is among the most popular photo sharing and storage applications that has surpassed 500 million users in May 2017 and continues to gain more users by the minute.
The top consumer electronics company, Apple uses a hefty amount of machine learning in its products. Its virtual assistant, Siri, is perhaps the most popular mobile virtual assistant in the world, and like Cortana and Google Assistant, she can interact with humans and perform the commands they are uttering. It also utilizes machine learning for its Face ID technology that offers a convenient way for its customers to sign in to their Apple devices and became a trendsetting technology that gave birth to a plethora of similar implementations in other devices. Apple’s media management system and digital store, iTunes, also uses machine learning to build its recommendation system that tailors its recommendation to each user’s media consumption behavior. As of 2017, iTunes remains the most preferred and most popular music streaming platform in the United States.
Its billions of users may not be fully aware of it, but Facebook uses plenty of machine learning for its social media operations. This is probably most evident in its automatic tagging feature, arguably the most popular and widely-used face detection and recognition implementations that automatically detects faces in uploaded photos, recognizes them and automatically suggests users be tagged. Its targeted ads feature also makes use of machine learning to learn the behavior of individual users and select personalized ads that it deems the user will be most interested in. This has made Facebook’s advertising platform highly effective in expanding the customer base and boosting the revenue of various businesses. Facebook’s friend recommendation feature also uses machine learning to determine who among the users that are not yet among an individual’s friend list are likely to be friends with him in real life. This has enabled Facebook to connect more and more people with their friends both old and new, and gives it an appeal that transcends generations.
All these companies we’ve taken a look at have integrated machine learning into many of the products and services they offer, allowing them to rise above their competition and remain at the forefront. With these companies processing huge volumes of data on a daily basis, machine learning has enabled them to take advantage of these data and continuously improve their services as more data come in.
Machine learning skills
Here are some of the most important skills that are expected of competent machine learning developers. For those wanting to enter the field of machine learning, having these skills will greatly increase your chances of being accepted by top companies.
The first step towards learning machine learning basics is learning to code and to code well. There are a large number of machine learning algorithms that developers can use, some of which are simple enough to code from scratch while others are so complex that they will require the use of libraries. Regardless of the complexity, though, a machine learning developer will always have to do a good deal of programming in order to extract data, process these data into a format that the algorithm can accept, receive and analyze the output of the algorithm, and then process the results and present them to the user. Typical programming languages used for machine learning include Python, R, Java, C, and C++.
Solid background in mathematics and statistics
Each of a large number of machine learning algorithms is best suited to a specific type of task or application, and understanding each of these algorithms is key to becoming a great and competent machine learning developer. Understanding these algorithms, however, requires an excellent grasp of many mathematical concepts, such as derivatives and integrals, gradient descent, and convex optimization, as well as statistical concepts, such as basic probabilities, normal distributions, Bayes nets, data science, and big data analytics.
Good understanding of software engineering
The typical job of a machine learning engineer or developer is to create a component that will be part of a bigger project, such as an application or software. Therefore, he/she will inevitably have to work in cooperation with other developers and engineers, and will subsequently have to understand how the different components fit together. Moreover, special considerations in the design of the code architecture must be made to ensure the smooth flow of the entire application. Additionally, knowledge of software engineering best practices, such as proper documentation, version control, and extensive testing will make machine learning codes more readable and maintainable.
Trends in machine learning
With machine learning being a hot topic both in the industry and the academe, research in the field is very active, and it is commonplace to see new algorithms being formulated every day. It is therefore important for a good machine learning engineer to remain updated with all these new algorithms in order to take advantage of the increase in performance, optimization, and resource consumption that they offer. Here are some of the hottest topics in machine learning that aspiring machine learning developers should pay attention to in order to stay relevant in the machine learning world.
One of the most promising areas of machine learning is deep learning, as it can yield remarkably high accuracies in performing various tasks given sufficiently large amounts of data, so-called big data. Deep learning architectures are inspired by the structure of the human brain and have been enhanced and improved in different ways using various mathematical operations to cater to different types of applications. With sufficiently powerful machines and sufficiently big data, deep learning can vastly outperform many of the other machine learning algorithms.
One of hottest fields of research in machine learning is in autonomous vehicles, more commonly known as self-driving cars. These vehicles have been trained to drive on their own, taking into consideration everything in their surroundings such as other vehicles, stop lights, road signs, and weather conditions. These vehicles can, therefore, roam the roads without a human driver. If such cars become successful, they could quite possibly usher in a new technological era, where humans would no longer be necessary to drive transportation vehicles.
Natural language processing
Natural language processing (NLP) is an active area of research in machine learning. It aims to allow computers to understand the human language just as any other human would. Currently, virtual assistants like Cortana, Siri, Alexa, and Google Assistant can understand a little bit of the human language, but they are nowhere near as good as humans in terms of comprehension. Advancements in NLP aim to improve this and eventually let machines understand humans just as good as, if not better than, an actual human would.
Machine learning is a breakthrough in artificial intelligence, as it enables machines to learn from experience rather than having to be programmed with every decision it should make for every situation. It’s being applied to all sorts of applications, including facial, speech, and object recognition; natural language processing; and predictive analytics. Unsurprisingly, it’s being aggressively used by the world’s largest companies, and demand for machine learning engineers and developers is at an all-time high. We are at the advent of creating intelligent machines and systems that can perform tasks we thought only humans could perform, and this idea has now become more than just a concept. If you want to make significant contributions to an industry that’s still in its infancy, now would be a great time to consider machine learning development for a career. While the field of machine learning has only recently started to take flight, it has a huge potential to grow and expand, and may even be bound to define the way humans would live in the near future.
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