Machine Learning Skills: Your Guide to Getting Started

Machine learning (ML) is a high-demand field with 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 programmes. Learn more about essential machine learning skills, machine learning jobs, and how to make a start in this lucrative field.

What is machine learning?

ML is a subcategory of artificial intelligence (AI) that uses algorithms and training data to replicate how humans learn. You accomplish this by using neural networks, sometimes called artificial neural networks, since they simulate neural systems in the brain. The program aims to improve its ability to complete a task as it receives more data, much like 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

One 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 it may have bugs or defects to work out in the output, the computer won't do anything outside of the direct instruction the programmer provides. 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 recognise patterns and adapt their output accordingly.

What is feature engineering?

Feature engineering is the process of collecting, analysing, 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 category 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 and non-technical competencies like communication and problem-solving.

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

Critical concepts in machine learning often involve statistical analysis and mathematical data manipulation. Some examples of this in software engineering include writing algorithms that can search, sort, and organise data sets. Developing algorithms is essential to working with the size of 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 and 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 modelling 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 from IBM.

Domain knowledge

ML development and engineering jobs require designing automated software with intuitive solutions clients use. Engineers should accurately understand clients' needs and design systems that meet them. The programmer must understand domains in-depth to create reliable features and solutions accordingly.

Where to start: Google Cybersecurity Professional Certificate: Led by an industry leader in technology, this career certificate programme 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 or interpersonal skills, workplace skills are essential to the machine learning professional toolkit. The following sections outline a few vital workplace skills for ML professions.

Communication skills

You’ll often work with a team of other data scientists, analysts, and developers to collaborate on projects. Many Agile development teams consistently deploy software and work with other members. Therefore, you must 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 analyse problems and use their knowledge to approach them effectively.

Time management

As a machine learning engineer, you must often multitask to meet customer, employer, and business demands and allocate time efficiently to tasks. You must also have excellent organisational skills for planning and executing projects and consider implementing solutions in a timely fashion. 

Teamwork

Engineers are often the keystone to project development. Agile development teams frequently collaborate with other mathematicians, engineers, marketers, and designers, so engineers must smoothly integrate other team members into operations to work together 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 Specialisation: Led by an industry leader in technology, this specialisation covers essential workplace skills from problem-solving and critical thinking to leadership and professional communication.

How to qualify for a career in machine learning

You have multiple options to explore on your career journey, starting with your education. Building a robust foundation in relevant subjects, developing your skill set, and considering certification to demonstrate your abilities are essential. 

Educational requirements

Educational requirements for ML professionals vary depending on the industry, company, and role's responsibilities. 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, maths, statistics, 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 the academic field.

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

Machine learning certifications

Employers often look for certifications to demonstrate your mastery of the knowledge and skills necessary 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

Professional Certificates are another way to boost your resume and add to your expertise. A few helpful options to consider include: 

• 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 to help you learn machine-learning skills. Online courses include those that provide you with the foundational knowledge of more advanced concepts. Upon completion, you can pursue courses and add those with a shareable certificate to your resume. You can also pursue bachelor's and master's degree programmes or take individual master's level classes. Below are a few examples to get you started:

Beginner courses:

• Machine Learning Specialisation, Stanford University and DeepLearning.AI

• Mathematics for Machine Learning Specialisation, Imperial College of London

• Machine Learning for All, University of London

• Machine Learning Introduction for Everyone, IBM

• AI For Business Specialisation, University of Pennsylvania

• AI For Everyone, DeepLearning.AI

Guided Projects:

• Unsupervised Machine Learning for Customer Market Segmentation

• Build a Machine Learning Web App with Streamlit and Python

• Automatic Machine Learning with H2O AutoMl and Python

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

Median annual salary in India: ₹11,00,000 [1]

In the current technical age, machine learning engineer jobs are in demand. In this role, you’ll 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?

As a machine learning engineer, you will design systems and create intuitive architecture for machine learning software. You must be familiar with machine learning techniques and algorithms and research and implement these tools accurately and effectively. 

You need discretion in selecting data, picking effective data representation methods, using quality data, and scientifically transforming it. You must stay updated with the field and continually improve and expand your knowledge. 

Other machine learning careers

If you want to pursue a path other than ML engineering, you’ll find plenty of other jobs in this field that can allow you to work with machine learning technology. Discover eight more machine learning careers to consider:

1. Data scientist

Data scientists examine which questions need answering and how to find the relevant data. You will assess company challenges and determine 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. You might find opportunities in various sectors, including 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. In this position, you try to simplify data as much as possible so users can digest it and use it for solutions. 

4. Artificial intelligence (AI) engineer

In an AI engineer position, you will develop intelligent algorithms capable of automation, learning, and analysing as a standalone system. You will also attempt to create systems capable of logic and reason like humans. 

5. Software engineer

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

6. Business intelligence analysts

As a business intelligence analyst, you will review 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

As a natural language processing scientist, you will use advanced algorithms to design and create machines that accurately replicate human speech and language patterns. You might also explore human-computer interactions to inform and improve upon ML algorithms and AI. 

8. Robotics engineer

A robotics engineer develops, configures, tests, and debugs robots and automated software. You’ll 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. By 2028, the leading 500 companies in India will require approximately 30,000 AI and ML experts, according to a study by IIM Ahmedabad [2]. 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 many companies are seeking but is only now starting to recruit for. 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. On Coursera, you’ll find many relevant courses on topics such as AI, machine learning, and various coding languages from top universities.

• 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 optimising and changing all facets of the industry, and getting familiar with these practices will help one stay ahead of the future development of this technology. Pursuing a career in this field will ensure you work with the latest technology.

• Machine learning careers offer variety: You can pursue various 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, familiarising 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 programmes, Professional Certificates, Specialisations, and foundational courses to broaden your knowledge at your own pace.

You can also enroll in the Machine Learning Specialisation program 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.