Machine learning (ML) is one of the essential skills required for today's software engineering and data science careers. If you are unsure whether machine learning is for you, consider reading on to learn more about the myriad uses of ML tools and techniques. Not only will machine learning open up many new career opportunities, but it can also provide numerous personal applications.

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.

Professional Uses for Machine Learning

Machine learning careers vary, from data science positions to financial analysis roles. Titles for ML professionals vary, too, but top careers include:

• Machine Learning Engineer
• Software Engineer
• Data Scientist
• Data Engineer
• Business Intelligence (BI) Developer
• Natural Language Processing (NLP) Scientist
• Software Developer

The most common responsibilities of these professionals also vary, depending on factors like sector or industry, experience, and the parameters of the position. Data Scientists use Python for machine learning to write algorithms, Software Engineers use ML to develop software, and Business Intelligence Developers use it for tasks like data visualization. Consider the following fields where machine learning is essential for many roles.

Data Science

The data science and analytics field comprises a wide swath of roles. Data Scientists, Machine Learning Engineers, and Data Analysts work with data to discover patterns, provide insights, and create solution-focused programs.

Machine learning in data science offers benefits for virtually every industry and sector, but the top areas include:

• Banking, Financial Services & Insurance (BFSI)
• Retail
• Healthcare
• Transportation
• Manufacturing

Because machine learning is often associated with the Python programming language, those new to the field typically learn Python. The level of Python training depends on the chosen field. While Data Scientists and Software Engineers typically need Python expertise, Business Intelligence or Financial Analysts may not.

Software Engineering

Software Engineering is the Swiss army knife of tech jobs. Many professionals with roles in development, programming, and even data science have software engineering skills. Whether a position is called Software Engineer depends on the sector or company within it.

While a majority of Software Engineers may not need ML skills, some decide to transition from software engineering roles to machine learning engineering roles. Software engineering teams may focus on code, whereas ML engineering teams may also need to deal with datasets and tracking models.

Read more about Software Engineers and ML in What to Learn After Software Engineering.

Business Intelligence (BI)

Many tech professionals use data visualization tools, especially in data science and analytics. Tableau and Power BI are among the most common. Among the most common careers that need BI tools are:

• Data Scientist
• Business Intelligence (BI) Developer
• Business Intelligence Analyst
• Data Scientist
• Data Visualization Specialist
• Data Visualization Engineer
• Data Analyst

Sometimes these titles overlap different roles, similar to the title Software Engineer. Again, this depends on the sector or company.

Many professionals today believe machine learning will profoundly affect BI tools. As more companies automate processes and use ML to gain insights, BI tools will need to be incorporated into business intelligence. If you’re interested in BI, consider learning ML tools and skills.

Other Uses for Machine Learning

Most of us now use technology powered by ML outside the context of work, whether we know it or not. Consider the following personal uses for machine learning in day-to-day living.

Search Engines/Product Recommendations

While Google is the granddaddy of search engines, more and more companies now use search engines within their platforms to make recommendations. Amazon, Netflix, and Spotify are some of the most obvious examples, but there are countless others. If you shop online, you will now frequently see questions on websites like “Did you mean…?” or suggestions like “You also might like…” These are all powered by machine learning.

Facial Recognition

Facial recognition software is no longer the province of law enforcement or government agencies. While many have banned facial recognition due to privacy concerns, millions of smartphone owners now use it instead of a password. Soon, facial recognition may help locate missing persons or even improve products. And like recommendation engines, it’s powered by ML.

Spam Filtering

Email may no longer be as popular as texting or messaging apps, but most people have an inbox that's never empty. Spam messages account for as much as 50% of all emails, a percentage that only promises to grow even higher.

With the advent of spam and email filtering tools, some relief is in sight. Machine learning powers many spam filtering tools, including spam protection from major corporations like Cisco and cybersecurity services from vendors like TitanHQ.

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, or ML, is one of the most well-known subcategories of artificial intelligence (AI).

• Popular careers requiring ML skills include:
  • Machine Learning Engineer
  • Data Scientist
  • Business Intelligence (BI) Developer
  • Software Engineer
  • Data Engineer

• Top fields where ML is essential include:
  • Banking, Financial Services & Insurance (BFSI)
  • Retail
  • Healthcare
  • Transportation
  • Manufacturing

• Top skills for ML professionals can include:
  •  Python
  • MySQL
  • Natural Language Processing (NLP)
  • Data visualization

• Professional uses for ML include:
  • Data science
  • Software engineering
  • Business intelligence (BI)

• Machine learning also powers many technologies for personal use, including:
  • Search engines
  • Product recommendations
  • Facial recognition software
  • Spam filtering

• You can receive comprehensive machine learning training through Noble Desktop, in person, or online.