If you don't think about it, the future can be a little daunting. It is full of AI, automation, 3D printing, virtual reality, IoT and other concepts that until now seemed like science fiction. But if you understand these ideas, it can also be a place full of opportunities. For example, if you understand the basics of AI and big data, you can master a career as a machine learning technician. That can not only give you a very healthy salary for machine learning engineer, but it can also help you shape that very future in the future.
In this post we are going to look at what a machine learning engineer does, why it is a great job and how you can get started.
Why machine learning?
Machine learning (ML) allows companies to use huge data sets for applications that had never been possible before. ML algorithms can learn the habits and buying behavior of customers, perform incredibly complex math and enable completely new products.
Almost every industry is strongly influenced by AI and machine learning in the near future and in ways that you probably would not expect. Take video games, for example, where machine learning has made real-time ray tracing possible, resulting in photo-realistic lighting. Every industry seems to be completely transformed by the marriage of data and logic.
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It is for this reason that Harvard Business Review named data scientist the "sexiest job of the 21 st century".
is a salary for machine learning engineering? According to Prospects.ac.uk, the average salary of a machine learning engineer in the UK is £ 52,000, which can be as high as £ 170,000 if you work for a company such as Google or Facebook. That is about $ 62,568 or $ 204,551.65, respectively.
A salary for machine learning can be up to $ 204,551
What is machine learning?
First, it is important to know what machine learning is and what is not.
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This is in contrast to artificial General Intelligence (AGI), an AI that is designed to have several different perform tasks and perhaps even pass the Turing test.
Computer vision, on the other hand – the ability of a program to identify objects in a scene – is achieved through machine learning. By viewing hundreds of thousands of photos, you can learn an AI & # 39; to recognize objects such as cars & plants. If your phone's camera has scene detection, this machine uses learning. Similarly, ML is also used to teach speech recognition virtual assistants.
Machine learning can be used to identify X-ray health problems and assist physicians with their diagnoses, or to make the weather more accurate. to predict. There is much more to use.
What does a machine learning engineer do?
The task of a machine learning engineer is to learn AIs and software using data.
The task of a machine learning engineer is to learn AIs and software using data. They can:
- Write programs and develop algorithms to extract meaningful information from large data sets
- Perform experiments and test different approaches
- Optimize programs to improve performance, speed and scalability
- Dealing with data engineering to ensure clean data sets
- Suggesting useful applications for machine learning
A machine learning engineer can therefore work for a company that is already producing a product – be it speech recognition, computer vision or a little more specialists. Alternatively, they can work for an agency that offers machine learning solutions to companies that can take advantage of the technology. Or maybe they work in the R&D department of a technology company like Google to create new applications.
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There is some overlap between the roles of a machine learning engineer and a data scientist. Similarly, you may have to rely on skills such as data mining, predictive analysis, maths, etc. However, the role of the ML engineer is more specific and applies that knowledge in a very specific way.
And of course the salary of the machine learning engineer is usually higher to reflect this.
To get an idea of the kind of things you need to understand as a machine learning engineer, I recommend this post about the top 10 algorithms used in ML. If that fascinates you, you will probably enjoy ML. If that is not the case, you may be better suited for a different role.
Interested in becoming a machine learning engineer? Do you think you have what it takes? This is what you need to know to get started and to get a great salary for machine learning engineer.
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In terms of qualifications and certifications, there is no set path to becoming an ML engineer. Many of the jobs that pay the best machine learning salaries require a bachelor's degree. This will often be a degree in computer science, which provides a broad understanding of computers, technology and programming. A degree in mathematics can also be a good starting point.
Ideally you should build on this with a background in software engineering and data science. The most useful programming languages in this area are Python, C and C ++.
From there you can switch to more specialized roles in machine learning, or adjust your resume with the machine learning courses below. Experience with ML APIs such as TensorFlow and Keras will also be very useful.
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Due to the huge amount of processing power and storage required to process the huge data sets related to machine learning, you are largely working with cloud-based systems. To this end, it is also important to demonstrate familiarity with distributed computing.
Because machine learning engineering is such a pioneering career, there is no way to go. You may even discover that as an autodidact you can go a long way if you can build up a strong enough CV.
Courses and Certifications
Here are some courses and certifications that you can use to advance as a machine learning engineer:
Bachelor of Computer Science – This is a complete online bachelor's degree from the University of London that provides the perfect foundation offers for those who can devote time. You study 3-6 years and you have to bet 14-28 hours a week.
Data Science: Machine Learning – If you already have some background in programming and / or mathematics, then add specific machine learning knowledge may be all you need. This is a free 8-week course from Harvard University. You can add a verified certificate for a small fee and it will also count towards a Data Science Professional certificate if you want to follow it further. You can find the full course here.
Foundations of Data Science: Computational Thinking with Python – Another free course, this time from Berkeley University of California. It is 5 weeks long and requires an obligation of around 4-6 hours a week. You can pay a little extra to add a verified certificate, or you can count it for a fully professional certificate in Foundations of Data Science.
Specialization machine learning – This specialization machine learning from the University of Washington consists of four separate courses and is free to register. You will receive a course certificate that you can add to your LinkedIn or CV.
Programming in C # – This exam from Microsoft is considered a credit for an MCSA, but also helps you fill your resume with proof of relevant coding skills all by yourself!
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Learn Python programming Masterclass – This Udemy course does not offer a professional certificate, but is an affordable and useful introduction to this much-needed programming language.
So there you have it! That is what you need to know to become a machine learning engineer. Is this a career that you would be interested in? Are you already an ML engineer? Share your tips and experiences in the comments below!