Machine Learning Skills: Your Guide to Getting Started

Machine learning (ML) is a high-demand field in which you can explore various career opportunities. Developing the skills you need to enter or advance a career in machine learning is possible through many avenues, including online coursework, certifications, and degree programs. Learn more about essential machine learning skills, machine learning jobs, and how to get started in this lucrative field with the following article.

What is machine learning?

ML is a subcategory of artificial intelligence (AI) focusing on the use of 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.

Read more: What Is Machine Learning? Definition, Types, and Examples

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 instruction 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 how to recognize patterns and adapt its output accordingly.

What is feature engineering?

Feature engineering is the process of collecting, analyzing, and manipulating raw data into "features," or, measurable inputs that can be used to train predictive models. For example, in a recommendation system for local restaurants, features can include customer ratings, price range, and type of food. Each of these categories helps the program learn which recommendations are the most relevant.

Core machine learning skills

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 as well as non-technical competencies like communication and problem-solving proficiency.

Technical 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.

Software engineering skills

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 work with the size data sets often involved with machine learning. It would help to be familiar with data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays.

Where to start: Google IT Automation with Python Professional Certificate: This beginner-friendly course provides an overview of the Python programming language as well as how to use it to automate real-world tasks.

Data science skills

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 skills and data science skills simultaneously with this beginner-friendly online course offered by IBM.

Domain knowledge

ML development and engineering jobs require designing automated software with intuitive solutions used by clients. Engineers should accurately understand and design systems that meet their 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.

Workplace skills 

Also known as soft skills or interpersonal skills, workplace skills are an essential part of the machine learning professional toolkit. The following sections outline a few of the most crucial workplace skills for ML professions.

Communication skills

Machine learning engineers often work with a team of other data scientists, analysts, and developers to collaborate on projects. Many Agile development teams deploying software consistently will have to work in conjunction with other members and should be able to communicate appropriately with team members. 

Problem-solving skills

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.

Time management

Machine learning engineers will often have to multitask demands from customers, employers, and businesses and must allocate time to tasks efficiently. An engineer must be organized in the planning and execution of projects and consider implementing solutions in a timely fashion. 

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.

Continued education

Another significant part of the engineering role is staying up to date with AI, deep learning, and machine learning fields that are developing and expanding at a rapid pace. An engineer in tune with the newest technologies, best practices, and developments should embrace change and growth in the industry. They should consider outside education sources through boot camps, classes, and certifications to continually grow and expand their knowledge to be an asset to their business or employer.

Where to start building workplace skills

People and Soft Skills for Professional and Personal Success Specialization: Led by an industry leader in technology, this specialization covers key workplace skills from problem-solving and critical thinking to leadership and professional communication.

How to qualify for a career in machine learning

Educational requirements

Educational requirements for ML professionals vary based on the industry, company, and 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 request a bachelor's degree, but you may need a master's or Ph.D. for more advanced roles or jobs in the academic field.

Read more: 8 Machine Learning Books for Beginners: A 2023 Reading List

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:

• Amazon SageMaker

• Microsoft Azure Machine Learning Studio

• Google Cloud AI Platform

• IBM Watson

• KNIME Analytics Platform

• SAS Viya

Read more: 7 Machine Learning Projects to Build Your Skills

Machine learning certifications

Employers often look for certifications to demonstrate your mastery of the knowledge, skills, and experience needed to succeed in a machine learning engineer position. Consider studying for and earning one or both of the following certifications:

• AWS Certified Machine Learning - Specialty: Validates your expertise in using machine learning models on Amazon Web Services (AWS)

• Google Cloud Professional Machine Learning Engineer: Certifies your foundational knowledge of machine learning and ability to create solutions for the cloud

Professional Certificates

• Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

• IBM Machine Learning Professional Certificate

• DeepLearning.AI TensorFlow Developer Professional Certificate

• IBM Applied AI Professional Certificate

• Google Data Analytics Professional Certificate

• Google Cloud Digital Leader Training Professional Certificate

Online courses to build ML skills

You have a wealth of coursework available for learning machine learning skills. Online courses include those that provide you with the foundational knowledge of more advanced concepts. Upon completion, you can pursue free courses and those with a shareable certificate to add to your resume. You can also pursue bachelor's and master's degree programs or take individual master's level classes. Here are a few examples to get you started:

Beginner courses:

• Machine Learning Specialization, Stanford University and 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

• AI For Everyone, DeepLearning.AI

Guided Projects:

• Life Expectancy Prediction Using Machine Learning

• Fake News Detection with Machine Learning

• Unsupervised Machine Learning for Customer Market Segmentation

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. This position holds eighth place on Indeed's Best Jobs of 2023, based on metrics such as job growth and salary. They test software, run statistical analyses, and shape ML applications and infrastructure based on a client's needs.

What are the skills required for a machine learning engineer?

A machine learning engineer designs machine learning systems and creates intuitive architecture for machine learning software. They must be familiar with machine learning techniques and algorithms and be able to research and implement these tools accurately and effectively. They need discretion in selecting data, picking the most effective data representation methods, using quality data, and scientifically transforming it. It is also essential that they stay up to date with the field and continually improve and expand their knowledge. 

Read more: What Is a Machine Learning Engineer? (+ How to Get Started)

Other machine learning careers

If engineering isn't the right career path for you, there are plenty of other jobs in this field that can allow you to work with machine learning technology. Here are eight more machine learning careers to consider:

1. 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. 

2. Data analyst

A data analyst collects, curates, cleans, and interprets data sets to answer a question or devise a solution to a challenge. These professions are prominent in business, finance, criminal justice, science, medicine, and technology. 

3. 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. 

4. Artificial intelligence (AI) engineer

An AI engineer has to develop intelligent algorithms capable of automation, learning, and analyzing as a standalone system. They attempt to create systems capable of logic and reason like humans. 

5. Software engineer

Software engineers design and create software that can attack problems. They design code to address a challenge and develop an intuitive solution for their clients. 

6. Business intelligence analysts

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.

7. NLP scientist

A natural language processing scientist works to design and create machines that accurately replicate patterns in human speech and language. 

8. 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 is just now starting to recruit. Familiarity and expertise in these systems will make an employee 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. Coursera offers many relevant courses from top universities on topics such as AI, machine learning, and various coding languages.

• 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 industry, and getting familiar with these practices will 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 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. 

Continue building your machine learning skill set with Coursera

Whether you are still considering a career in machine learning, just entering the field, or working in a related field, increasing your knowledge of machine learning is beneficial. Consider taking courses in machine learning to broaden your skill set or help you decide if this is the right career for you. You might explore machine learning courses on Coursera that include degree programs, Professional Certificates, Specializations, and foundational courses to broaden your knowledge at your own pace.

Or, enroll in the Machine Learning specialization from Stanford University and DeepLearning.AI, designed to equip learners with job-ready skills. You’ll gain an understanding of supervised and unsupervised learning, as well as best data management practices and case studies for a well-rounded introduction.