Artificial intelligence and machine learning are at the top of many lists of the most critical skills in today’s job market. According to Indeed, machine learning is one of the most in-demand careers in 2021, with ML being #1 among the best jobs to hunt for in the USA. Indeed's data shows a 344% rate of growth and a median salary of more than $145,000 per year. The Covid-19 pandemic has significantly boosted the demand for computer and information technology jobs, with employment projected to grow 11% in the next decade. The potential for professionals in machine learning is enormous. “AI, Machine Learning, and automation will power the creation of 97 million new jobs by 2025,” says The World Economic Forum. So, if you see that machine learning is a field that you can grasp, now is a great time to seriously consider building your career in that area.
For those unfamiliar with the field, a quick introduction might be in order. Machine learning is a subset of artificial intelligence (AI) that focuses on analyzing and interpreting data to enable learning and decision-making outside of human interaction. A machine learning career is great for those interested in data, automation, and algorithms, as a career in ML is often filled with analyzing a large amount of data and automating it. Whether you are just about to pursue a career in IT or are an experienced IT professional interested in a career switch, machine learning welcomes professionals of all levels. However, not knowing how to start a career in ML can lead you in the wrong direction. In this article, we will tell you how to build a career in machine learning.
How To Build a Career in ML
Building a career in an emerging field like ML can be challenging even for experienced IT professionals. It requires a dedicated approach. Artificial intelligence and machine learning come with specific qualifications and requirements. To build a career in ML, you need to possess a wide variety of skills ranging from advanced knowledge in math and statistics to programming and data modeling skills.
How To Start Career in AI
If you are intrigued by this field and wonder how to start a career in AI, we are about to describe the learning paths for ML professionals.
If you are new to this field and wonder how to get a job in machine learning as a fresher, we recommend starting with mathematics and statistics and taking courses in programming, especially Python or R. To gain an entry-level job in ML, you need to be exceptionally good at writing algorithms and using them to solve real-life problems. Below are the essential steps needed to start a career in ML:
- Develop a solid foundation in statistics, linear algebra, calculus, and probability
When it comes to prerequisites, mathematics and statistics play a key role. You should know statistics measures (mean, median, variance), analysis methods (hypothesis testing, ANOVA), and distributions (normal, uniform, binomial, Poisson). As for probability, make sure you are comfortable working with conditional probability, likelihood, independence, Bayes’ rule. Speaking of mathematics, you need to know differential and integral calculus, partial derivatives, directional gradient, Hessian, Laplacian and Lagrangian Distribution, matrices, vectors, and linear transformations.
While the amount of knowledge needed in math and statistics may seem overwhelming at first, once you have grasped the basic technique of ML, the process becomes much easier.
- Learn to code in R, SQL, Java, Python, C++
Being able to code is one of the critical skills when it comes to Machine Learning. Employers are usually looking for programmers who are proficient in several programming languages. Proficiency in Python and/or R and their associated libraries is a must for ML engineers. In addition, we highly recommend considering learning other programming languages such as Java, C++, and SQL to stand out from the crowd.
- Learn data modeling and evaluation
Data modeling is a tool or algorithm used to estimate the structure of the data set. Understanding data modeling is essential for ML algorithms, processing, and data exploration. You should know how to find patterns (clusters, correlations, eigenvectors), predict the properties of previously unseen instances (classification, anomaly detection), and determine the right accuracy/error measure and evaluation of strategy (sequential vs. randomized cross-validation, training-testing split).
Machine Learning Courses
Although a Machine Learning engineer is a job you can get without extensive formal schooling if you have the required skills, it would still be hard to break in without completing online courses on the discipline. There is no shortage of online training programs offering ML certifications, each taking a different instructional approach and focusing on different areas of machine learning. Therefore, it is important to analyze the artificial intelligence courses available and determine which one best suits your needs and interests. Below are some courses for you to consider:
- Coursera – one of the most popular, highly-ranked online courses on ML offered by Stanford University;
- EdX – another well-known platform that hosts online university-level courses;
- Udemy – perfect for beginners as it provides detailed instruction in both Python and R;
- Udacity – a reliable program that will help you to gain must-have skills in analytics and data science.
What Does the Machine Learning Career Path Look Like?
Now that we covered the required skills and knowledge to become an ML professional, here are the top three promising ML career paths that you can aspire to:
ML Software Engineer
The machine learning engineer career path is one of the most promising in the field of AI. ML engineers are primarily involved in designing and developing self-running ML systems to automate predictive models by using ML tools and algorithms. They are responsible for performing statistical analysis and optimizing machine learning models and algorithms. ML engineers typically act as a bridge between data science and the construction of machine learning systems.
The Data Scientist and ML engineer roles are somewhat similar, considering both roles involve processing a large amount of data and dealing with similar technologies. However, while ML engineers focus on designing and developing AI systems and predictive models, data scientists collect, analyze, and extract meaningful insights from large datasets. In our previous article, we discussed how to start a career in data science.
NLP scientists apply algorithms to identify and extract natural language rules to convert the language data into forms that machines can easily understand. NLP scientists are primarily responsible for designing applications that can understand human languages. The goal of an NLP scientist is to create systems that can understand and comprehend human languages as quickly as humans can.
Business Intelligence Developer
Business Intelligence developers are engineers who use BI software to interpret data and share valuable insights with decision-makers in their organizations. They are responsible for creating software tools to improve the company’s research process and guide business strategies.
Human-centered ML designer
Human-centered ML designers are responsible for developing and deploying information systems that can learn from and collaborate with humans through information processing and pattern recognition. Human-centered ML designers create these ML systems that end up requiring minimal or no human intervention down the road.
If you feel like you already have the necessary education skills to get into machine learning, now is the time to look for a machine learning job. Ask yourself, what niche in machine learning you are particularly interested in and move in that direction. Most of the jobs in ML can be found in enterprise IT departments. However, many organizations often turn to tech recruitment companies such as Employa to find the best talent. And these days, there are plenty of job openings for you to consider.