One essential aspect of receiving professional machine learning training to set yourself up for a career change is learning what kinds of fields and industries will require you to use your newfound skills.

Each industry will have different requirements and expectations for prospective employees, and you should consider these before you begin training. This consideration is especially true for students who have an idea of the kind of work they want and those who aren’t yet sure what path to take after learning machine learning skills.

What is Machine Learning?

Machine learning (ML) is one of the best-known subcategories of artificial intelligence (AI). This complex multidisciplinary field can require training in programming languages like Python, databases like MySQL, and natural language processing (NLP). Common ML careers include Machine Learning Engineers, Data Scientists, and Business Intelligence (BI) Analysts.

Machine learning is often associated with Python programming and data science. Popular uses of ML in daily activities include voice recognition tools like Siri, recommendation lists from Amazon or Netflix, and user engagement icons on platforms like Instagram and TikTok. 

Read more about what machine learning is and why you should learn it.

What Can You Do with Machine Learning Skills?

Machine learning algorithms dominate today’s internet. Websites gather information based on search, social, and shopping. Top ML applications include:

• Social media - Meta Platforms was one of the first well-known companies to use ML to measure user activities. Other social media platforms using ML include Twitter and TikTok.

• Recommendation Engines - If you use Amazon or streaming services, you’ve seen the You May Like feature. Companies like Apple and Netflix use ML algorithms to customize experiences.

• Natural Language Processing (NLP) - Analyzing text includes steps like identifying the language, syntax parsing, and sentiment analysis. Machine learning is essential to NLP.

Common Industries That Use Machine Learning

Future employment estimates for machine learning professionals can be challenging to gauge. Machine Learning Engineers, Data Scientists, and Financial Analysts are all examples of roles that can require substantial ML tools and techniques.

The U.S. Bureau of Labor Statistics (BLS) includes ML under the general umbrella of Computer and Information Research Scientists. Positions in this category include everyone from Data Scientists to Web Developers. The BLS projects a 21% growth rate in this broad category over the next decade, much faster than the average career. Common requirements for these tech pros include Python, analytical and mathematics skills, and a strong computer science education.

Banking, Financial Services & Insurance (BFSI)

Banking, financial services, and insurance, or BFSI for short, covers a range of related service industries. Among the many sectors requiring machine learning tools and techniques, BFSI is one of the largest.

While a comprehensive list of ML applications for BFSI is beyond the scope of this article, the following categories include some of the most essential.

• Fraud detection - Fraud detection is crucial in both banking/finance and insurance services. Many of the world's largest banks have implemented ML for fraud detection.

• Risk management - The concept of risk management involves an organization's ability to withstand economic shocks, such as financial devastation caused by a market crash or major natural disaster. ML technology drives risk management in many entities, taking the guesswork out of the equation.

• Custom financial services - From consumer banking to wealth management advisory services, customization is becoming the norm. Machine learning algorithms allow companies to personalize the customer journey in meaningful ways to retain clients.

Healthcare

Experts seem to agree that artificial intelligence is reshaping the healthcare landscape. Machine learning algorithms allow healthcare professionals to manage data, find patterns, and even diagnose and treat conditions.

Among the many ML applications in healthcare are the following:

• Clinical practice guideline development
• ML algorithms for imaging studies (MRI, CT, X-ray)
• Medication development and research
• Automated billing systems

The employment outlook for healthcare continues to grow, with an estimated 13% growth rate projected from 2021 to 2031, much faster than the average sector. With the ongoing shortage of RNs, LPNs, and other nursing staff, the need for ML technology will likely increase whether or not enough nurses join the workforce.

Retail & ecommerce

The broad retail space, including ecommerce and brick and mortar entities, makes up a large category influenced by machine learning. With ecommerce, ML algorithms drive everything from chatbots to product recommendations. In-person retail outlets also feel the effect, with management decisions influenced by predictive models, financial analysis, and other ML tools.

Retail consists of multiple subsectors. The U.S. Bureau of Labor Statistics provides information on their growth outlooks, from motor vehicle parts and dealers to clothing and personal care products. Whether you work in ecommerce or manage a sporting goods store, consider the many benefits machine learning can offer in the retail space.

Learn Machine Learning Skills with Noble Desktop

Noble Desktop offers in-person and live online bootcamps and certificates featuring machine learning (ML), like:

• Data Science Certificate - This program provides data science fundamentals before advancing through ML, Python for automation, and Structured Query Language (SQL).

• Python Machine Learning Bootcamp - Programmers already comfortable with Python data science can save by taking this bootcamp as part of the Data Science Certificate.

• Python Data Science & Machine Learning Bootcamp - This bootcamp combines ML and Python training modules from the Data Science Certificate but without the SQL bootcamp. 

• Check out Noble Desktop's full-time and part-time data science programs here.

Key Takeaways

• Machine learning (ML) is one of the best-known subcategories of artificial intelligence (AI).

• Each industry has different requirements for prospective employees, and it's essential to know yours before studying machine learning.

• Popular roles for ML pros include:
  • Data Scientist
  • Machine Learning Engineer
  • Business Intelligence (BI) Analyst
  • Natural Language Processing (NLP) Scientist
  • Data Engineer

• Top ML applications include:
  • Social media
  • Product recommendations
  • Voice recognition tools
  • Chatbots
  • User engagement icons

• The BLS projects a 21% growth rate for Computer and Information Research over the next ten years, a category that includes machine learning.

• Top industries benefiting from ML include:
  • Banking, Financial Services & Insurance (BFSI)
  • Healthcare
  • Retail and ecommerce

• You can receive comprehensive machine learning training through Noble Desktop, either in person or online. Popular courses include:
• Data Science Certificate
• Python Machine Learning Bootcamp
• Python Data Science & Machine Learning Bootcamp