Key takeaways
-
Machine learning (ML) skills include both technical skills, such as software engineering and data science, and workplace skills, such as communication and problem-solving.
-
Some prerequisite machine learning engineer skills include algebra, coding, and analytics.
-
You can gain the skills required for machine learning engineer and other ML jobs through a degree in computer science or data science.
To improve your machine learning skills, consider taking online courses and certificates, and embrace continual learning through boot camps and workshops. Read on to discover essential machine learning skills you should consider developing when you’re interested in this type of career and how to start in this lucrative field.
Afterward, if you’re ready to start building your machine learning skills, consider enrolling in Stanford and DeepLearning.AI’s Machine Learning Specialization. In as little as two months, you’ll have the opportunity to gain experience with building ML models with NumPy and scikit-learn, training supervised models for prediction and binary classification tasks, and applying the best practices for ML development. Upon completion, you’ll earn a shareable certificate to add to your resume.
What is machine learning?
Machine learning is a subcategory of artificial intelligence (AI) focusing on using algorithms and training data to replicate how humans learn. This is accomplished through the use of neural networks, sometimes referred to as artificial neural networks, since they are designed to simulate neural systems in the brain. The goal is for the program to improve its ability to complete a task as it receives more data, the same way humans improve skills through practice and study. ML algorithms can be as simple as a recommendation list on a streaming app or as complex as self-driving cars.
Machine learning engineering vs. software engineering
The most significant difference between traditional and machine learning programming is automation. In software engineering, the computer parses and executes code according to the developer’s instructions. Although there may be bugs or defects to work out in the output, the computer won’t do anything outside of the direct instructions the programmer provides it. Conversely, ML uses automated processes to learn how to respond to input on its own based on the developer’s rules. Over time, ML programs learn to recognize patterns and adapt their output accordingly.
What are the prerequisite skills required for machine learning engineer jobs?
Some of the requirements for machine learning engineers are basic mathematical skills, such as algebra, coding, and analytics.
What skills are needed for machine learning? 7 core skills to master
Essential concepts in ML often involve statistical analysis and mathematical data manipulation. Machine learning professionals excel in technical skills such as software engineering and data science, and nontechnical competencies like communication and problem-solving.
4 technical machine learning skills
In the following few sections, you’ll find brief explanations of essential technical skills for machine learning professionals and recommendations for how to start building them.
1. Software engineering
The most important concepts in machine learning often involve statistical analysis and mathematical data manipulation. Some examples of this in software engineering would be writing algorithms that can search, sort, and organize data sets. Developing algorithms is essential to working with the size of data sets often involved in machine learning. It would help to be familiar with data structures such as stacks, queues, graphs, trees, and multidimensional arrays.
Where to start:Google IT Automation with Python Professional Certificate: This beginner-friendly course provides an overview of the Python programming language and how to use it to automate real-world tasks.
2. Data science
Data science languages like Python, SQL, C++, R, and Java are also helpful for data modeling and statistical analysis. Skills in mathematics, probability, and statistics are another way to develop your machine learning knowledge.
Where to start:Python for Data Science, AI & Development: You can build your programming and data science skills simultaneously with this beginner-friendly online course offered by IBM.
3. Domain knowledge
ML development and engineering jobs require designing automated software with intuitive solutions that clients use. Engineers should accurately understand and design systems that meet client needs. The programmer must understand domains in depth to create reliable features and solutions for the client.
Where to start:Google Cybersecurity Professional Certificate: Led by an industry leader in technology, this career certificate program focuses on network architecture, information security (InfoSec), and cybersecurity frameworks so you can learn to design secure computer systems.
4. Neural networks
Neural networks are layered ML programs that make decisions and are, therefore, crucial to deep learning models. They learn how to reach a decision from training data that teaches them accurate outputs for the input being requested. Thanks to the speed at which they operate and the accuracy they can reach after significant training, they can group data at fantastic rates.
Where to start:Johns Hopkins Applied Machine Learning Specialization. In this three-course series, you’ll continue expanding your ML foundation by learning how to build neural networks from scratch and apply them to real-world datasets like MNIST.
3 workplace machine learning skills
Also known as soft skills or interpersonal skills, workplace skills are an essential part of a machine learning professional’s toolkit. The following sections outline a few of the most crucial workplace skills for ML professionals.
1. Communication
Machine learning engineers often work with a team of data scientists, analysts, and developers to collaborate on projects. Many Agile development teams deploying software consistently will have to work in conjunction with other team members and should be able to communicate effectively with them.
2. Problem-solving
Machine learning requires developers to approach challenges critically and develop creative solutions. Machine learning developers must analyze problems and use their knowledge to approach them effectively.
3. Teamwork
Engineers are often the keystone to the development of projects. Agile development teams are often assembled with other mathematicians, engineers, marketers, and designers, so engineers must smoothly integrate other team members into operations to collaborate on a project.
How to get started in a machine learning career
There are a few ways to begin pursuing a career in machine learning.
Educational requirements
Educational requirements for ML professionals vary based on the industry, company, and the role’s responsibilities. In general, you should have a strong foundation in statistics, algorithms, big data, and programming languages. Examples of degrees for aspiring machine learning professionals include computer science, data science, or a related field. Many ML jobs require a bachelor’s degree, but you may need a master’s or PhD for more advanced roles or jobs in academia.
Essential tools and programs
As someone who works with machine learning technology, you must be familiar with machine learning platforms to build programs and solve problems. These can include:
-
Microsoft Azure Machine Learning Studio
How to improve machine learning skills
To continue building on your foundational skill set and advance what you know, you may want to enroll in online courses or certificates designed to continue strengthening your knowledge. Upon completion, you often get a shareable certificate to add to your ML resume or LinkedIn profile. Here are a few examples to get you started:
-
AI For Everyone, DeepLearning.AI
-
Machine Learning Specialization, Stanford University & DeepLearning.AI
-
Mathematics for Machine Learning Specialization, Imperial College of London
-
Machine Learning for All, University of London
-
Machine Learning Introduction for Everyone, IBM
-
AI For Business Specialization, University of Pennsylvania
-
Supervised Machine Learning: Regression and Classification, Stanford & DeepLearning.AI
-
Mathematics for Machine Learning and Data Science Specialization, Stanford & DeepLearning.AI
Machine learning jobs
With a background in machine learning, you can pursue various careers that focus on artificial intelligence and data science. Consider how the following aligns with your interests and career goals.
Machine learning engineer
One of the most in-demand machine learning jobs is a machine learning engineer. ML engineers test software, run statistical analyses, and shape ML applications and infrastructure based on a client’s needs. They must be familiar with machine learning techniques and algorithms and be able to research and implement these tools accurately and effectively. It is also essential that they stay up to date with the field and continually improve and expand their knowledge.
Data scientist
Data scientists examine which questions need answering and how to find the related data. They have to assess challenges for a company and figure out how to acquire the relevant data to address these issues.
Data engineer
Data engineers build systems to collect, manage, and convert data into a comprehensive data set for data scientists and analysts to interpret. They try to simplify data as much as possible so it can be digested and used for solutions.
Artificial intelligence (AI) engineer
An AI engineer has to develop intelligent algorithms capable of automation, learning, and analysis as standalone systems. They attempt to create systems capable of logic and reason like humans.
Software engineer
Software engineers design and create software that can solve problems. They design code to address a challenge and develop an intuitive solution for their clients.
Business intelligence analyst
A business intelligence analyst reviews data for financial reports and marketing information. These reports establish patterns and assess economic trends to influence a company’s decisions, operations, and goals.
NLP scientist
A natural language processing scientist works to design and create machines that accurately replicate patterns in human speech and language.
Robotics engineer
A robotics engineer develops, configures, tests, and debugs robots and automated software. They work with the installation and calibration of machines to ensure robotic devices operate correctly and safely.
Why work in machine learning?
Machine learning is an in-demand field with competitive salaries and opportunities to work with the latest technology. Consider the following benefits of developing your machine learning skills:
-
Demand for machine learning skills is high: Machine learning development is a highly sought-after skill that many companies are looking for, but are just now starting to recruit. Familiarity and expertise in these systems can make a candidate stand out to a potential employer or client.
-
Opportunities for continual learning: Many resources are available to help you continue your knowledge and develop your skill set through online courses, boot camps, certifications, and workshops. You’ll find many relevant courses from top universities on topics such as AI, machine learning, and various coding languages on Coursera.
-
Work at the cutting edge of technology:Machine learning and AI genuinely change how computation, mathematics, and technology operate in the real world. Machine learning is optimizing and changing all facets of the industry, and getting familiar with these practices can help one stay in front of the further development of this technology in the future. Pursuing a career in this field will ensure you work with the latest technology.
-
Machine learning careers offer variety: You can pursue a variety of careers with a machine learning background. A skill set where you are proficient in machine learning development and the project lifecycle will improve job security. Due to the high demand for machine learning professionals, familiarizing yourself with these practices could lead to many exciting and fulfilling careers.
Support your professional growth with actionable learning tools
Keep up with industry trends and network with other professionals by subscribing to Career Chat, our weekly LinkedIn newsletter. Then, explore free digital resources for your machine learning career:
-
Find a course:Which Machine Learning Course Should You Take? Find Out in 1 Minute
-
Build upon skills:Artificial Intelligence Glossary: Learn AI Vocabulary
Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses.
Build job-ready skills with Coursera Plus
Start 7-day free trial
Start 7-day free trial
