Applied Machine Learning: What It Is and Why It Matters

Applied machine learning (ML) is expanding rapidly as artificial intelligence (AI) evolves. According to data from Grand View Research, the ML market will grow at a compound annual growth rate (CAGR) of 35.6 percent from 2023 through 2030 [1]. This significant growth offers a good indicator of demand for skilled professionals. Additionally, as AI continues to progress and evolve into artificial general intelligence, the demand for ML professionals will grow. The growing scale of AI and ML models will heavily impact the future of various industries. 

Explore the evolving world of applied ML, discover its applications in different organizations, and learn more about how to begin a career in the industry. 

What is applied machine learning?

Applied machine learning is the application of ML techniques to complete tasks without programming. 

ML refers to a division of AI that enables computers and machines to replicate the way humans learn, enhancing model performance and accuracy by pulling from past datasets. ML enables AI machines and computers to derive knowledge from data and learn from it without the need for human intervention. 

What is the difference between theoretical and applied machine learning?

Machine learning theory is the study and understanding of learning as a computational process, while applied machine learning is the implementation of ML techniques. 

Machine learning theory involves creating mathematical models to analyze learning challenges better and understand the principles of using algorithms to help computers learn and improve over time. The field also provides guarantees for algorithms and ensures that each algorithm works as intended. 

Applied machine learning is the implementation of ML algorithms to produce resolutions for various tasks, such as predicting future outcomes and streamlining operations. 

Who uses applied machine learning?

Various industries utilize applied ML, including finance, health care, education, and genetics. 

• Finance professionals: Financial industries utilize ML applications to perform fraud detection, automate trading, and enable clients to use advising services. 

• Health care professionals: Applied ML in health care enables hospitals to optimize administrative tasks, calculate disease risks, and enhance decision-making processes. 

• Educational institutions: ML applications can help streamline scheduling, track student progress, and enhance educational tasks and processes. 

• Scientists: ML techniques enable researchers to identify genetic variants to better understand genomes. 

Various industries broadly use machine learning applications to predict outcomes, identify patterns, and interpret trends in data. By leveraging advanced algorithms, machine learning can quickly process massive amounts of data, enabling organizations to make efficient decisions based on sound data. These tasks include categorizing, classifying, and analyzing data at speeds and efficiencies much faster than humans. 

As a result, ML has earned its place as an invaluable tool for businesses, health care providers, financial institutions, and more. Its ability to automate complex processes and develop invaluable insights allows organizations to optimize operations, improve customer experiences, and become more competitive in the marketplace.

Benefits of applied machine learning 

Applied machine learning has the potential to offer various benefits, including streamlined data processing, the implementation of predictive analytics, fraud detection, and advanced marketing techniques. 

• Streamlined data processing: Machine learning techniques enable computers and machines to process massive amounts of data. 

• Predictive analytics: By analyzing past data sets, businesses can identify trends using applied machine learning and use predictive analytics to forecast future outcomes. 

• Fraud detection: Machine learning tools enable systems to perform fraud detection by identifying irregular patterns and flagging unusual activity. 

• Advanced marketing: By analyzing past data, machine learning algorithms are able to personalize advertisements and suggestions for users, streamlining marketing and customer service. 

Disadvantages of applied machine learning

Some disadvantages of applied machine learning include a lack of emotional connection, high equipment costs, data security risks, and biases in training data. 

• Lack of emotional intelligence: Machine learning models lack personal connection and may decrease the development of social skills because of decreased human-to-human interaction, especially in educational settings. 

• High equipment costs: Machine learning applications require high-quality technology, equipment, and programs that organizations may not be able to afford. 

• Data security risks: Organizations must invest in high-quality security protocols to perform preventative measures and protect confidential information, such as medical records. 

• Bias in training data: If professionals train machine learning algorithms on flawed or biased data, the data it reproduces will perpetuate these biases. 

How to begin your career in applied machine learning

Getting started in applied machine learning involves networking and developing your education. Explore some common steps in more detail to begin planning your career path.

Networking with industry professionals 

Networking with machine learning professionals may help you gain valuable insights and could offer potential career opportunities. Attending events, conferences, and seminars may enable you to exchange ideas and information with other professionals and expand your knowledge of the industry. Joining online communities and forums is another good way to connect with professionals and build an online presence that may attract potential employers.

Expanding your education

To gain a deeper understanding of applied machine learning and start building a career in the field, pursuing an education in computer science or data science can be a beneficial step. Many institutions typically require a formal educational background, such as a bachelor’s or master’s degree. Attending college will not only provide foundational knowledge but also offer hands-on experience in a supportive environment, enabling you to develop essential skills for the industry.

If you don’t have the means to attend college, various online courses are available to you at a lower price. Pursuing courses in computer science, data science, machine learning, and artificial intelligence will help you gain a better understanding of applied machine learning tasks. 

Learn applied machine learning on Coursera.

Applied machine learning has the potential to benefit various industries, such as finance, health care, and education. Expand your knowledge of machine learning algorithms on Coursera with the University of Michigan’s Applied Machine Learning in Python, or learn more about best practices for machine learning development with Stanford and DeepLearning.AI’s Machine Learning Specialization.