Artificial intelligence (AI) is a very hot issue today, but the attention it’s getting overshadows its subfields, such as machine learning. Machine learning (ML) is a complex, much-in-demand field that covers everything from generating suggestion lists for a streaming app to making self-driving cars a reality.
This article covers machine learning skills, everything from the basics to more complex, focused disciplines such as machine learning engineering skills. We will also cover machine learning skill prerequisites, the benefits of machine learning skills, and how to increase your machine learning skill repertoire through an ML course. This article focuses on how these skills relate to a machine learning engineer career.
Let’s define the technology first, then move on from there.
What is Machine Learning?
Machine learning is a subgroup of artificial intelligence where programming focuses on algorithms and large amounts of data to help the machine imitate how people learn tasks and improve its skills without human intervention. So, machines learn independently, bypassing the human element or explicit programming. Machine learning describes an algorithm ingested with vast data, extracting patterns found within and turning those patterns into actions.
Machine learning often overlaps with deep learning and data science.
Also Read: Is AI Engineering a Viable Career?
What’s the Difference Between Understanding Machine Learning and Traditional Engineering?
Toss around the term “engineer” enough, and it morphs into this all-encompassing concept that ignores subtleties and distinctions between the different disciplines. Machine learning engineers differ from traditional engineer programmers and are in a class alone because the machine learning developer handles all the standard programming.
The computer stays within the parameters outlined by the developer within the code. Machine learning uses automated processes, giving input and receiving the output. The computer learns how to deal with the problem based on the rules the developer sets down and provides output based on those rules. Eventually, the machine learns to recognize patterns and adapt its output accordingly.
All types of engineering involve building something, but a machine learning engineer builds datasets and algorithms that help software learn independently.
What Tasks and Roles Does a Machine Learning Engineer Need to Master?
Here is a list of roles and tasks a machine learning engineer must understand and know how to perform.
- Design machine learning systems
- Create intuitive architecture for machine learning software
- Acquire familiarity with machine learning algorithms and tools
- Research, develop, and implement machine learning tools and algorithms accurately and effectively
- Select data carefully, choose the most effective data representation methods, use only quality data, and scientifically transform it
- Test software, run statistical analyses, and shape applications based on the client’s needs
- Remain current in machine learning and continually improve and expand their knowledge
How does a machine learning engineer handle all these tasks? By acquiring the proper skills, which we present below!
Machine Learning Skills: The Breakdown
It’s time to break down the machine learning engineer skills required of every ML professional. Machine learning is a complex field that demands a lot from its professionals. We’ll break down the skills into “hard” technical skills and “soft” workplace skills. The former requires study, and the latter requires practice and developing the right mindset.
Technical ML Skills
You acquire these skills through education and online ML programs.
- Data Modeling. Data modeling involves understanding data’s underlying structures and finding patterns not apparent to the naked eye.
- Statistics. Tools and tables are critical for helping machine learning create models from data. Statistics and associated branches, such as hypothesis testing and variance analysis, are crucial for building ML algorithms.
- Probability. Most machine learning algorithms involve working in uncertain conditions. Probability helps ML engineers predict future results and consequences and is a valuable resource for algorithm design.
- Programming Fundamentals. Machine learning works with large data sets, so ML engineers need strong fundamental programming skills in disciplines like algorithms, computer architecture, data structures, and complexity. And speaking of programming, that segues nicely into the following requirement.
- Machine Learning Programming Languages. Machine learning resembles object-oriented languages and isn’t limited by any programming language. Therefore, machine learning can be developed in any language that meets the required components. So, machine learning engineers should be adept in at least one of the following: C/C++, Python, and R.
- Machine Learning Algorithms. Machine learning algorithms are typically divided into Supervised, Unsupervised, and Reinforcement Machine Learning. So, machine learning engineers should become familiar with ML algorithms such as:
- Naïve Bayes Classifier
- K Means Clustering
- Support Vector Machine
- Apriori Algorithm
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Natural Language Processing. Natural Language Processing, or NLP, teaches human languages, including their complexities, to computers, enabling the latter to interpret and understand human languages and aiding learning.
Some people are natural-born communicators, while others must work harder to master such skills. It’s possible to acquire soft skills through practice and discipline.
- Communication. Machine learning professionals understand the concepts they work with. That’s a given. However, the trick is to convey these complicated ideas to non-technical laypeople. Management, shareholders, and clients must understand what the ML engineer is doing. Additionally, since most ML engineers work in teams, everyone on that team should master sufficient communication skills to facilitate collaboration.
- Time Management. ML engineers are beset on all sides with tasks, assignments, and deadlines. Time management covers planning, multi-tasking, prioritizing, scheduling, and using limited time efficiently.
- Problem-Solving. Machine learning entails solving a succession of problems and challenges. Developers must critically approach these ML problems, analyze them, and devise creative solutions.
- Teamwork. This soft skill goes hand in hand with communication skills, but it’s distinctive enough to warrant its own call-out. Agile ML teams typically work with other designers, engineers, mathematicians, and marketers, so engineers must smoothly integrate these team members into operations to collaborate on the ML project.
Now let’s look at the necessary qualifications and prerequisites expected of machine learning engineers.
The Qualifications and Prerequisites for Machine Learning Professionals
Machine learning engineers typically require at least a bachelor’s degree in computer science or a parallel field like computer programming, mathematics, or data science. If you want to advance your ML career further, consider continuing your education to the master’s and doctoral levels.
Machine learning engineers should also have a strong foundation in algorithms, statistics, big data, and programming languages.
Also, machine learning engineers should get familiar with different ML performs like:
- Amazon SageMaker
- Google Cloud AI Platform
- IBM Watson
- KNIME Analytics Platform
- Microsoft Azure Machine Learning Studio
- SAS Viya
But there’s more! Machine learning professionals benefit greatly from online ML training programs and certifications.
Machine Learning Courses and Certifications
The better trained the machine learning professional, the more in demand they are, and the easier it is to find the perfect position. Consider the following machine learning professional certifications and courses.
- AWS Certified Machine Learning
- DeepLearning.AI TensorFlow Developer Professional Certificate
- Google Cloud Digital Leader Training Professional Certificate
- Google Cloud Professional Machine Learning Engineer
- Google Data Analytics Professional Certificate
- Google IT Automation with Python Professional Certificate
- IBM Machine Learning Professional Certificate
- IBM Applied AI Professional Certificate
- Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Also Read: AI ML Engineer Salary – What You Can Expect
What Are the Career Benefits of Machine Learning Skills?
Artificial intelligence and machine learning are big deals today, especially as present-day organizations increasingly adopt the technologies. Even if your career isn’t directly related to machine learning, it’s wise to adapt your skill set to the current trends and acquire a fundamental understanding of ML concepts.
However, if you decide to go “all in” with machine learning, that knowledge can carry you down many different but rewarding career paths, such as:
- Artificial intelligence (AI) engineer. AI engineers are responsible for developing intelligent algorithms capable of automation, learning, and analyzing as standalone systems. They create systems that are capable of logic and reasoning like humans.
- Business intelligence analysts. Business intelligence analysts review data for marketing information and financial reports. These reports establish patterns and assess economic trends that eventually influence an organization’s company’s decisions, goals, and operations.
- Data analyst. Data analysts collect, clean, curate, and interpret data sets to answer questions or design a solution to a challenge. Data analysts are in heavy demand in business, criminal justice, finance, medicine, science, and technology.
- Data engineer. Data engineers create systems that collect, convert, and manage data into comprehensive data sets for data analysts and scientists to interpret. They simplify data so it can be digested and used for solutions.
- Data scientist. Data scientists research questions that need answering and how to locate the related data successfully. Data scientists assess the organization’s challenges and determine how to acquire the relevant data to address these issues.
- NLP scientist. Natural language processing scientists design and create machines that accurately replicate human speech patterns and language.
- Robotics engineer. Robotics engineers develop, configure, test, and debug automated software and robots. They install and calibrate machines to ensure the robotic devices operate safely and correctly.
- Software engineer. Software engineers design and create software that solves problems. These engineers design code that addresses challenges and develop intuitive solutions.
How to Learn More About Machine Learning
Now that you see the importance of machine learning skills and upskilling, why not check out this online machine learning program? This post graduate AI and machine learning program offers six months of applied learning with over two dozen projects and integrated labs. Online classes will teach you valuable concepts like prompt engineering and generative AI.
According to Glassdoor.com, machine learning engineers in the United States earn a yearly average of $151,933.
So, prepare for a rewarding career in this new and growing technology. Get your training and take those first steps into the exciting world of machine learning!