A career in Machine Learning is quite in trend these days. All thanks to the emerging use of Data Science & Technology. If you have a keen interest in Machine Learning and want to build a career in it, you should definitely read this article.
According to employment-related search engine Indeed, the average salary base for machine learning jobs is $146,085.
With a 344 percent growth in job postings, machine learning has a wide array of opportunities for its aspirants. Several types of industries are moving towards this technology – transportation, manufacturing, energy, finance – and machine learning are exploding, with smart algorithms being used everywhere from smartphone apps to marketing campaigns.
But before we dive head-first into discussing a career in machine learning, let’s breakdown a few basics.
What is Machine Learning?
To wrap our heads around the concept of machine learning, we need to understand artificial intelligence (AI). In computer science, AI is how we make intelligent machines. Machine learning (ML) is an application of artificial intelligence that enables systems to learn and improve a task that they are not programmed to do through experience.
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The process involves using a set of high-quality training data and building various machine learning models via different algorithms. The primary aim is to capacitate computers to learn automatically without any human intervention.
Top Career Paths in Machine Learning
Now that we have tackled the fundamentals, we can explore popular options related to a career in machine learning. If you’re looking for an in-demand profession, it would be a wise move to set yourself up with the skills to work with smart machines and artificial intelligence. Given below are some prominent and well-paying opportunities available to a student pursuing machine learning.
1. Machine Learning Engineer
A machine learning engineer is required to run various machine learning experiments using programming languages such as Python, Java, Scala, etc. The job requires a strong aptitude for probability and statistics, data modeling, machine learning algorithms, and system design.
ML engineers use the principles from computer science and engineering in mathematics acquired from their machine learning degree to design and develop software.
Machine learning courses can equip students with the expertise to write software programs for different purposes such as operating systems, network distribution, and converting programs into executable files.
Careers in machine learning, such as software engineering, require proficiency in Java, C++, C. The salary for a software engineer starts at $69,000, and the average yearly pay is $104,000.
2. Data Scientist
A data scientist uses advanced analytics technologies such as machine learning and predictive modeling to collect, analyze and interpret large amounts of data and produce actionable insights. Consequently, machine learning is an extremely crucial and important skill for a data scientist besides other skills like data mining and in-depth knowledge of statistical research techniques, etc. Furthermore, a data scientist also requires expertise in big data platforms such as Hadoop, Pig, Hive, and Spark and programming languages like SQL, Python, Scala, and Perl.
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The position of data scientist incorporates machine learning, and their average pay is about $121,000.
3. NLP Scientist
Natural Language Processing (NLP) is a subdivision of computer science and artificial intelligence that involves the interaction between computers and human languages. It requires providing machines with the ability to understand natural human linguistics and communication.
NLP technology is the driving force behind common applications like Google Translate, Grammarly, and personal assistant applications like Siri, Cortana, and Alexa. It is particularly booming in the healthcare industry, media, finance, and human resources among many others.
NLP scientists help in the creation of a machine that learns patterns of human speech and translates spoken words into other languages. Therefore, they require fluency in syntax, spelling, and grammar of at least one language in addition to machine learning to equip a machine with the same skills.
The average salary of an NLP Scientist ranges from approximately $83,933 to $138,712. However, this varies from one organization to another, depending on the size of the company.
4. Human-Centered Machine Learning Designer
As the name suggests, human-centered machine learning focuses on machine learning algorithms that are centered around humans. It involves the development of various systems that can perform human-centered machine learning based on information processing and pattern recognition and allows machines to ‘learn’ the preferences of individual humans without the requirement of cumbersome programs.
Human-centered machine learning is responsible for the algorithms behind Facebook, Twitter, and Instagram feeds. It is also used for YouTube and Netflix recommendations. Various online retailers like Amazon use it to decide what products to show you.
Machine learning courses can help build your foundation in understanding how a computer learns, preparing you for a promising career in machine learning. Designers of human-centered machine learning are also involved in creating software for banks, as an increasing number of banking transactions are conducted online and electronically. This position pays an average of $97,000 per year and can top out at $125,000.
How Should you Start a Career in Machine Learning?
Start small—look for low-hanging fruit and trumpet any early success. The best executives exploit machine learning as a tool to craft and implement a strategic vision.
The most important thing is to keep on learning, not for just a few months but over several years. Here are some things you can start doing today to position yourself for a future career in machine learning.
1. Understand What is Machine Learning
This may seem obvious, but it’s imperative that before you begin a career in machine learning, you first have a clear understanding of what it is.
Florian Douetteau, CEO of computer software company Dataiku, says,
“Having experience and understanding of what machine learning is, understanding the basic maths behind it, understanding the alternative technology, and having experience — hands-on experience — with the technology is key.”
It’s also vital to introspect and self-evaluate. Do you like to learn with hands-on projects? Are you driven and self-motivated? Can you commit to goals and see them through? If so, you will enjoy studying and pursuing a career in machine learning.
2. Be Curious
Machine learning and AI are modern technologies that will only continue to evolve in the future, therefore a healthy sense of curiosity and love of learning is essential to pursue a career in this field.
According to Douetteau,
“Machine learning, as a demand, evolved quite rapidly in the last few years with new techniques, new technology, new languages, new frameworks, new things to learn, which made it very important for people to be eager to learn. Meaning, get online, read about new frameworks, read new articles, take advantage of online courses, and so forth. Trait number one if you want to be successful as someone working in machine learning is to be curious.”
3. Translate Business Problems into Mathematical Terms
Machine learning is a field designed for logical and cerebral minds with superb reasoning and analytical skills. A career in machine learning is a blend of technology, math, and business analysis into one job. You need to be able to focus on technology a lot and have a healthy intellectual curiosity. But you must also have this openness toward business problems and be able to articulate a business problem into a mathematical machine learning problem and bring value at the end.
4. Be a Team Player
“I manage products, not people,” is a habitual remark made by many supervising technical teams. It’s dead wrong. The term ‘machine learning’ may propel images of a lone worker surrounded by computer systems and machines. But it’s far from the truth in the present climate of collaborative teamwork in the machine learning industry.
CEO Florin Douetteau shares,
“Today, when you are working in machine learning, you are most likely working as part of a team, and this team would comprise people who have direct interaction with the business. So, it means if you want to be successful as a machine learning practitioner today, you must be ready and able to interact with the business and be a team player.”
5. Find Mentors
One of the most underutilized and effective ways to help people grow in a machine learning career is through forming mentorships. Find strong mentors from your network, or from academic institutions that you trained at in the past. To establish a stable career in machine learning would initially require you to take some guidance and direction from other well-experienced professionals. Don’t underestimate the power of being under the wing of a knowledgeable and insightful mentor. You’ll be pleasantly surprised that most people are willing to help out and give some of their time.
6. Ideally, have a Background in Data Analysis
In Data Analytics and Machine Learning Fundamentals LiveLessons, experienced CCIEs Robert Barton and Jerome Henry discuss artificial intelligence and the various families of machine learning and how they relate to the world of data analytics.
Data analysis extracts meaningful insights from various data resources, and machine learning fits within its spectrum. Both require somewhat similar skills such as data modeling, computational programming, and mathematical statistics.
Furthermore, American business magazine, Forbes also states in an online article that data analysts are in the perfect position to transition into a career in machine learning as their next step. According to the article, a role in machine learning requires an analytical mindset that can comprehend what works and what doesn’t.
7. Learn Python & How to use Machine Learning Libraries
You don’t have to be a programming genius to establish a successful career in machine learning. For a smooth machine learning journey, it’s necessary to choose the appropriate coding language right from the start, as this will determine your future. You must think strategically and prioritize correctly.
According to the Canadian online platform Towards Data Science, it’s quite sufficient to master one coding language for building a career in machine learning, and Python is the perfect choice for beginners.
It is a minimalistic and intuitive language with a full-featured library line (also called frameworks) that significantly reduces the time required to get your first results.
After learning a programming language, you can dive into machine learning libraries. Many data scientists recommend Scikit-learn and TensorFlow as some popular options.
8. Take Online Courses or Attend a Data Science Boot Camp.
Your goal should be to broaden your machine-learning-related skill set as much as possible. Some people have secured jobs in machine learning by various massive open online courses in the field. To enhance your prospects and opportunities, you need to go a step further. Pursuing additional courses in Data Science specialization, Data Analytics, Python Programming, Big Data Hadoop, etc. can help you bolster and strengthen your knowledge and expertise in machine learning. Moreover, participating in machine learning projects and online machine learning competitions can also provide you with the required experience and practical skills.
CEO Florin Douetteau offers some concrete suggestions:
“Start learning by mixing online courses and tutorials with machine learning competition. Going on, for instance, Kaggle, which is a website where you’ve got machine learning competitions. Another approach, if you’ve got the time and money, another approach that is getting pretty popular, is to get to a data science boot camp to accelerate the learning process.”
The Future of Machine Learning
Machine learning provides a competitive edge to an enterprise – be it a top MNC or a startup. Tasks that are currently being done manually will be performed by machines tomorrow.
American business magazine Forbes asserts:
“What is clear from the advantages of using AI within a business is that a majority of companies are actively working on a roadmap for handling data (68 percent), yet only 11 percent of these companies have completed this task. The models which are the most successful today are those which allow certain tasks to be taken over by AI whereby machine learning can acquire more information from and predict consumer behavior. Current ML models allow for rapid iteration of data and they deliver quick, reliable data sets that impact directly on the culture of work for businesses involved in any sort of real-time analytics, data integration and management, sales/revenue forecasting, and personal security and data processing.”
Here’s a hard-hitting fact – machine learning is allowing us, humans, to perform the more interesting facets of our jobs as AI carries out the mundane aspects of operations such as data mining. It’s time we accept machine learning for what it offers us rather than worrying about what it might take away. ML is being used across various industries from healthcare to education, and it is showing no sign of slowing down.
If you are inspired by the opportunity provided by Machine Learning, enroll in Digital Vidya’s Machine Learning Course today.
