Important points
Careers in machine learning (ML) require technical and workplace skills, from data science expertise to the ability to collaborate with others.
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Popular machine learning roles include robotics engineer, data scientist, and business intelligence analyst.
Learn about the key machine learning skills you should consider developing if you’re interested in this type of career, and how to get started in this lucrative field. Then, if you want to start exploring machine learning, consider enrolling in Stanford University and DeepLearning.AI’s machine learning specialization.
What is machine learning?
Machine learning is a subcategory of artificial intelligence (AI) that focuses on replicating the way humans learn using algorithms and training data. This is achieved through the use of neural networks, also known as artificial neural networks, as they are designed to simulate the neural systems in the brain. The goal is for programs to improve their ability to complete tasks as they receive more data, just as humans improve their skills through practice and learning. ML algorithms can be as simple as a recommended list for a streaming app, or as complex as a self-driving car.
Machine learning engineering and software engineering
The biggest difference between traditional programming and machine learning programming is automation. In software engineering, computers parse and execute code according to instructions from developers. The output may have bugs and flaws to work out, but the computer does nothing other than the direct instructions provided by the programmer. 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 required skills for a machine learning engineer?
Machine learning engineer requirements include basic mathematical skills such as algebra, coding, and analysis.
Machine learning core skills
Fundamental concepts of ML often include statistical analysis and mathematical data manipulation. Machine learning professionals excel in technical skills such as software engineering and data science, as well as non-technical competencies such as communication and problem-solving skills.
4 important technical skills
The next few sections briefly outline the technical skills important to machine learning professionals and recommendations for getting started building them.
1. Software engineering
The most important concepts in machine learning often involve statistical analysis and mathematical data manipulation. An example of this in software engineering is creating algorithms that can search, sort, and organize data sets. Algorithm development is essential to deal with the size of datasets often involved in machine learning. Familiarity with data structures such as stacks, queues, graphs, trees, and multidimensional arrays is helpful.
Where to start: Google IT Automation with Python Professional Certificate: This beginner’s 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 such as Python, SQL, C++, R, and Java also help with data modeling and statistical analysis. Mathematics, probability, and statistics skills are another way to improve your machine learning knowledge.
Where to start: Python for Data Science, AI, and Development: Build your programming and data science skills at the same time with this beginner-friendly online course from IBM.
3. Domain knowledge
ML development and engineering jobs require you to design automated software with intuitive solutions for your clients to use. Engineers must accurately understand and design systems that meet their needs. Programmers need a deep understanding of the domain to create reliable features and solutions for their clients.
Where to start: Google Cybersecurity Professional Certification: Led by technology industry leaders, this career certification program focuses on network architecture, information security (InfoSec), and cybersecurity frameworks to help you design secure computer systems.
4. Neural network
Neural networks are important to deep learning models because they are layered ML programs that make decisions. They learn how to arrive at decisions thanks to training data that tells them the exact output for the requested input. Thanks to the speed of operation and the accuracy that can be reached after sufficient training, data can be grouped together with amazing speed.
Where to start: Johns Hopkins University Applied Machine Learning Specialization. In this three-course series, you continue to expand your ML foundation by learning how to build neural networks from scratch and apply them to real-world datasets such as MNIST.
3 important skills in the workplace
Workplace skills, also known as soft skills or interpersonal skills, are an important part of the machine learning professional toolkit. The following section outlines some of the most important workplace skills for ML professionals.
1.Communication
Machine learning engineers often collaborate on projects with teams of other data scientists, analysts, and developers. Many agile development teams that consistently deploy software need to work collaboratively with others and must be able to communicate well with team members.
2. Problem solving
Machine learning requires developers to critically approach challenges and develop creative solutions. Machine learning developers need to analyze problems and apply their knowledge to address them effectively.
3. Teamwork
Engineers are often at the heart of project development. Because agile development teams are often made up of other mathematicians, engineers, marketers, and designers, engineers need to smoothly integrate other team members into operations and collaborate on projects.
How to start a career in machine learning
There are several ways to start pursuing a career in machine learning.
Educational requirements
Educational requirements for ML professionals vary by industry, company, and role responsibilities. In general, a strong foundation in statistics, algorithms, big data, and programming languages is required. Examples of degrees to become a machine learning expert include computer science, data science, or a related field. Many ML jobs require a bachelor’s degree, but more advanced roles and jobs in academia may require a master’s or doctoral degree.
read more: What can you do with a computer science degree? 10 in-demand fields
Essential tools and programs
Those who work with machine learning technology need to be familiar with machine learning platforms to build programs and solve problems. These include:
How to improve your machine learning skills
To further strengthen your foundational skill set and deepen your knowledge, we recommend enrolling in an online course or certification designed to continually enhance your knowledge. Once completed, you will often receive a shareable certificate to add to your ML resume or LinkedIn profile. Here are some examples to get you started.
Program for beginners:
Intermediate program:
machine learning jobs
With a background in machine learning, you can pursue a variety of careers focused 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 that of a machine learning engineer. ML engineers test software, perform statistical analysis, and shape ML applications and infrastructure based on client 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 important to stay up-to-date with the latest information in the field and to continually improve and expand your knowledge.
data scientist
Data scientists consider what questions need to be answered and how to find relevant data. They need to assess the company’s challenges and figure out how to obtain relevant data to address these issues.
data engineer
Data engineers build systems that collect, manage, and transform data into comprehensive data sets that can be interpreted by data scientists and analysts. We try to simplify the data as much as possible so that it can be digested and used in solutions.
Artificial Intelligence (AI) Engineer
AI engineers need to develop intelligent algorithms that can be automated, learned, and analyzed as standalone systems. They try to create systems that are capable of logic and reasoning just like humans.
software engineer
Software engineers design and create software that can attack problems. They design code to address challenges and develop intuitive solutions for their clients.
business intelligence analyst
Business intelligence analysts review data for financial reports and marketing information. These reports establish patterns and assess economic trends that influence corporate decisions, operations, and goals.
NLP Scientist
Natural language processing scientists work to design and create machines that accurately replicate human speech and language patterns.
robot engineer
Robotics engineers develop, configure, test, and debug robots and automation software. They assist with machine installation and calibration to ensure that robotic equipment operates correctly and safely.
Why do machine learning?
Machine learning is a high-demand field with competitive salaries and opportunities to work with the latest technology. Consider the following benefits of improving your machine learning skills.
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Continuing learning opportunities: There are many resources available to help you continue your knowledge and develop your skill set through online courses, boot camps, certifications, and workshops. Coursera offers many relevant courses from top universities on topics such as AI, machine learning, and various coding languages.
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Work with cutting edge technology: Machine learning and AI truly change the way computation, mathematics, and technology work in the real world. Machine learning is optimizing and changing every aspect of the industry, and becoming familiar with these practices will help you stay ahead of further developments in this technology in the future. Pursuing a career in this field will ensure you have access to the latest technology.
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There are many different machine learning careers. With a background in machine learning, you can pursue a variety of careers. Having a skill set that is well-versed in machine learning development and project lifecycle increases job security. Machine learning professionals are in high demand, so becoming familiar with these practices can lead to many exciting and fulfilling careers.
Build machine learning skills
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