Applied Machine Learning: What It Is and Why It Matters

Key takeaways

Applied machine learning (ML) is the application of machine learning techniques to complete tasks, such as predictive analytics, without programming. 

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

• Applied machine learning automates complex processes and develops invaluable insights, allowing organizations to optimize operations, improve customer experiences, and become more competitive in the marketplace.

• You can use applied machine learning to predict outcomes, identify patterns, and spot trends leading to important outcomes such as fraud detection, improved disease risk calculations, and streamlined completion of education tasks and processes.  

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. If you’re ready to learn fundamental AI concepts and develop practical machine learning skills, consider enrolling in the Machine Learning Specialization. You’ll have the opportunity to build skills in decision tree learning, model evaluation, deep learning, and more in as little as two months.

What is applied machine learning?

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

ML refers to a division of artificial intelligence (AI) that enables computers and machines to replicate how humans learn, enhancing model performance and accuracy by pulling from past data sets. 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 guarantees 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. 

What are the 4 types of machine learning?

Four types of machine learning are supervised, semi-supervised, unsupervised, and reinforcement, though you may find other categories, such as self-supervised, as well.

Consider the following brief overview of four common types of machine learning.

• Supervised: Machine learning models are trained on labeled data

• Unsupervised: Models are trained on unlabeled data

• Semi-supervised: Models are trained on both labeled and unlabeled data

• Reinforcement: Models are trained using a “reward and punishment” system based on their actions toward an established metric

Who uses applied machine learning?

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

• Financial professionals: The financial industry utilizes ML applications to detect fraud, automate trading, and enable clients to use advisory 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 understand genomes better. 

Learn more: Machine Learning for Risk Management

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 detect fraud by identifying irregular patterns and flagging unusual activity.

• Advanced marketing: By analyzing past data, machine learning algorithms can 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 be unable 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 they reproduce 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 industry knowledge. 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 provide foundational knowledge and 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 at a lower price. Pursuing computer science, data science, machine learning, and artificial intelligence courses can help you better understand applied machine learning tasks. 

Explore free machine learning resources

Join Career Chat on LinkedIn to stay current with the latest trends in your career field. Or, continue your learning journey with our other free digital resources:

• Read our Career Chat issue: 6 Machine Learning Certificates + How to Choose the Right One for You

• Watch on YouTube: Your Machine Learning Roadmap: What to Learn and When

• Take a quiz: Which Machine Learning Course Should You Take? Find Out in 1 Minute

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