If you’ve been fascinated by machine learning development and admired artificial intelligence in robots since childhood, then now is an excellent chance for you to become a specialist in this field! Learn how voice assistants like Siri work, and maybe even develop your own in the near future.
Although machine learning is still a relatively new profession, it is rapidly breaking into every possible ranking of popular professions of the future. Machine learning engineers can expect higher salaries than ordinary programmers, and require additional essential skills as well. In this article, we will look at the requirements to be a machine learning professional, whether it is necessary to get a university degree, and who else can work with these skills.
We hope that this article will help you choose the right direction in this field!
Machine-Learning Engineer Degree, Do I Need it?
Machine learning is still a very new profession, and few universities have introduced a machine learning engineer degree. Since the main requirements for this profession are mathematics, big data science, computer science, and programming, a person who wants to become an engineer should first get a bachelor’s degree in one of these areas. It will give you the necessary skills needed for further, more in-depth study. An education in similar sciences, such as physics, may also come in handy.
You won’t be able to become an engineer right away. However, if your long-term goal is to specialize in machine learning, you can try your hand at an associated profession in the interim.
“Machine learning is something every developer needs to know about.”— Kevin Scott, Microsoft CTO
What can you do with machine learning? A career in software development built on fundamental knowledge offers many opportunities:
- Software Engineer
- Software Developer
- Big Data Specialist
What is a machine learning engineer? A machine learning engineer primarily creates and analyses programs to help a company make the most efficient and automated use of its data. In this field, you can only achieve great heights if you are continually improving your skills and work quality. This field is constantly changing, so it is crucial to form a circle of acquaintances in machine learning to exchange experiences and knowledge. This way, you can always stay on top of current trends and not be confined to your personal experience.
In fact, like everywhere else, constant knowledge improvement is needed here. A great first opportunity is an internship at a large company. Pay attention to the availability of training programs at universities or companies. Professionals love to share their experiences, and often happily hold webinars and conferences.
Shortcut Careers: What to Focus on
Artificial intelligence career paths in machine learning can go many different ways. You can choose one direction on your own and specialize in it, or you can become a generalist and study all directions at once. Let’s elaborate on the three main directions.
“Artificial Intelligence, deep learning, machine learning —whatever you’re doing if you don’t understand it —learn it. Because otherwise, you’re going to be a dinosaur within three years.”— Mark Cuban, an American entrepreneur
Neural networks allow computers to train themselves. Neural networks are a tool that machines can use to analyze and classify information for learning. Software using neural networks can be trained to recognize text in images, faces, or objects. Machines can also make accurate predictions based on big data using neural networks.
Here are the critical areas for the application of neural networks:
- Data input and analysis. Voice command recognition, voice text input into the device.
- Security. Face and various biometric data recognition, counterfeit detection.
- Internet. Creation of smart news feeds, automated blocking of spam.
- Communications. Increasing coding and decoding speed.
Natural Language Processing
NLP is a field of machine learning that uses computers to understand, analyze, manipulate, and generate human language.
NLP gives machines the ability to understand human language. Soon, machines will learn to respond in a way that humans can interpret. NLP has the potential to fundamentally change the way computers and humans interact with each other.
Deep learning focuses on machine learning tools to solve problems through decision-making. In this method, information is processed through neural networks. It resembles the workings of the human brain.
Deep learning is the use of neural networks with numerous neurons and interconnections. It is currently the best possible way to mimic the human brain. Deep Learning Engineers were the most popular jobs posted on Indeed between 2018 and 2019.
And when you read about advances in computing, you’re probably reading about advances by scientists in artificial intelligence, which uses some form of deep learning.
Requirements for a Machine Learning Career
To become a machine learning engineer, you first need to be an experienced programmer. Job postings for machine learning engineers do not normally require machine learning experience. Because the profession is still very young, employers will be looking for applicants among experienced programmers with advanced analytical skills.
“Machine learning engineers must have a lot of knowledge to work effectively in a development team. Well-run large corporations give their specialists every opportunity for development and training.”— Dmitriy Tkachenko, Machine Learning Lead at Proxet, a custom software development company
However, not every programmer can become a proficient machine learning engineer. It helps to have a mathematical mindset and experience analyzing and working with big data. We suggest you review the general and specific requirements of the profession to know which skills you should pay attention to first and which skills are worth developing in the future. We hope you got answers to “Is machine learning a good field?”
There is a standard list of essential requirements in whichever branch of machine learning development you choose. Let’s take a closer look at them.
- You could use a bachelor’s degree in programming, mathematics, physics, or big data analysis. With this base, it will be easier for you to develop further skills.
- Advanced data modeling skills.
- It would be best if you had experience programming in Python, R, or Java.
- Experience in an Agile environment will also be beneficial in organizing workflows in your area of expertise.
- Your mathematical skills should be as developed as possible, which will help with further development.
- Strong written and verbal communication to interact with development teams.
Computer science fundamentals essential to machine learning engineering include data structures, algorithms, computability and complexity, and computer architecture.
You should have practical experience in creating software to analyze large amounts of data.
Career Paths with Resources: Software Engineer and Software Developer, Data Scientist
With the skills required for a machine learning engineer, you can also pursue other professions, and it won’t be a problem figuring out how to get a job in AI. Let’s take a look at where your knowledge is useful and how you can develop in related fields.
A software engineer must write a program that details how a computer should perform certain functions. Software engineers must apply fundamental principles of computer science to the design and development of software.
Various systems must undergo rigorous testing. If errors are found, the software engineer must examine the code to find and correct the problem.
Programming skills are a crucial part of a data scientist’s work, and this knowledge base is useful for future work. If you are interested in how to become an AI researcher, it will be useful for you to study what data scientists do.
Data scientists work with big data sets and perform the following operations:
- searching for patterns in information sets;
- preparing data for modeling (sampling, cleaning, feature generation, integration, formatting);
- modeling and visualization of data;
- developing and testing hypotheses to improve business metrics through the construction of machine learning models.
Software developers are responsible for creating flowcharts that help programmers do their jobs. They are generally considered the creative minds behind computer programs.
Software developers make it possible to check that updates are working correctly. They also provide documentation for the software. Their main tasks are strategic planning and creating a model for how the whole system will work.
Proxet is ready to provide solutions for machine learning software development for your business. With years of experience in machine learning, the company can help you achieve success.