Machine Learning Skills: Your Guide to Getting Started

Machine learning (ML) is an in-demand field that tends to pay well. 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.

In this article, we'll discuss essential machine learning skills you should think about developing when you're interested in this type of career and how to get started in this lucrative field. Afterward, if you'd like to begin exploring machine learning, consider enrolling in Stanford and DeepLearning.AI's Machine Learning Specialization.

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.

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.

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.

4 essential 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.

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 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.

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 skills 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 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.

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 thanks to 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 on your ML foundation by learning how to build neural networks from scratch and apply them to real-world datasets like MNIST.

3 essential 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.

1. Communication

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. 

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 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

Build or advance ML 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:

Beginner programs:

• 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

Intermediate programs:

• Applied Machine Learning Specialization, Johns Hopkins University

• Machine Learning Professional Certificate, IBM

• AI Engineering Professional Certificate, IBM

• Mathematics for Machine Learning and Data Science Specialization, Stanford & DeepLearning.AI

• Deep Learning Specialization, Stanford & DeepLearning.AI

• Machine Learning on Google Cloud, Google

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 the eighth place on Indeed's Best Jobs of 2023, based on metrics such as job growth and salary. 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 analyzing as a standalone system. They attempt to create systems capable of logic and reason like humans. 

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. 

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.

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 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. 

Build your machine learning skills 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.

• Break into machine learning with Stanford and DeepLearning.AI's Machine Learning Specialization. This three-course series is beginner-friendly. You'll learn how to apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection.

• Prepare for a career in machine learning with the IBM Machine Learning Professional Certificate. In this six-course series, you'll continue expanding your ML knowledge, including by learning how to compare and contrast different machine learning algorithms by creating recommender systems in Python.

• Continue advancing your ML skills with the Johns Hopkins Applied Machine Learning Specialization. In this three-course series, you'll advanced methods in machine learning applications and work closely with neural networks and models.