Why Learn Machine Learning?

Machine Learning (ML) is a broad field in technology used in many industries including Banking, Retail, and Healthcare. Careers in ML are vast and include roles such as Machine Learning Engineers, Data Scientists, and Business Intelligence Analysts.

Key Insights

  • Machine Learning (ML) is one of the fastest-growing fields in the business world today.
  • Top career opportunities in ML include Machine Learning Engineer, Data Scientist, Data Architect, Artificial Intelligence (AI) Engineer, and Cybersecurity Analyst.
  • ML algorithms have a significant presence on the internet, influencing customer journeys, business processes, and even employee job satisfaction.
  • Industries most reliant on machine learning technology include Banking, Financial Services & Insurance (BFSI), Retail, Health & Wellness, Information Technology (IT) Services, and Cybersecurity.
  • ML is a critical component in computational linguistics, NLP science, and design.
  • Essential skills for ML professionals include Python, SQL, Tableau, and Power BI.
  • Noble Desktop offers comprehensive machine learning training through immersive bootcamps and certificate programs, available both in-person and live online.

Have you ever explored everything machine learning (ML) can do? From facial recognition software to customized product recommendations, machine learning is one of the broadest, hottest tech fields today. Machine Learning Engineers, Business Intelligence (BI) Developers, and Natural Language Processing (NLP) Scientists are just a few of the many positions requiring ML skills.

Here, you’ll learn more about the types of careers that benefit from machine learning and how it can enhance your professional life. Regardless of your overall goals, learning about machine learning is a valuable and impressive skill to add to your resume.

What is Machine Learning?

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

Machine learning is often associated with Python programming and data science. Supervised, unsupervised, and reinforcement learning are the top three models of ML algorithms. 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?

Machine learning algorithms dominate today’s internet. Websites gather information based on everything you do online, from your search patterns to previous purchases, social media posts, and whether or not you abandon a product in a cart. As ML algorithms continue to influence our personal and professional lives, more and more businesses use them to streamline processes and determine customer and client journeys. The following are a few of the most popular machine learning applications.

  • Social media - Meta Platforms (formerly Facebook) was one of the first well-known companies to use ML to measure user activities. Examples of how they analyze statistical activity include their user engagement, chatbots, and content filtering features. Other top social media platforms using ML extensively include Twitter, Pinterest, and TikTok.
  • Product Recommendations - If you’ve ever bought a product from Amazon or subscribed to a streaming service, you’ve probably seen the You May Like feature. Companies ranging from Apple to Netflix use machine learning algorithms to customize your experience.
  • Natural Language Processing (NLP) involves text analytics and functions combined with machine learning. Analyzing text includes basic steps like identifying the language and more complex steps like syntax parsing and sentiment analysis. ML is essential to text analytics and NLP solutions.

Common Professional Uses for Machine Learning

While Data Scientists and Data Analysts use machine learning to collect and interpret data, Business Intelligence (BI) Analysts and Financial Analysts may analyze data sets differently and for different reasons.

Machine Learning Engineers, Software Developers, and Software Engineers also benefit from machine learning algorithms. They may develop everything from applications to platforms. These high-level professionals typically have a solid understanding of numerous disciplines—from programming languages to computer architecture.

Machine learning is vital in computational linguistics, NLP science, and design, along with top industries like banking, retail, and healthcare.

Gain Valuable Training

Whether you’re interested in training to be a Data Scientist or planning a career as a Machine Learning Engineer, there are plenty of excellent reasons to expand your knowledge of machine learning. Today, ML algorithms power everything from Amazon product recommendations to personalized investment advice from your bank or brokerage firm. The tools and skills you can gain from training in data analytics, deep learning, and data visualization can help you move forward in your current position or gain a foothold in a brand-new career path.

If you want to prepare for a new career, ML training can be valuable across many industries. Whatever sector your job falls within, consider the benefits of a program like the Data Science Certificate from Noble Desktop. Here, you’ll learn Python programming basics, apply machine learning models with the Scikit-learn library, and analyze data with NumPy and Pandas. This intensive course prepares ML novices for entry-level positions in data science or Python engineering roles.

The demand for ML professionals promises to continue its already rapid growth. According to the U.S. Bureau of Labor Statistics, computer and IT positions, including machine learning, will grow 15% between now and 2031. Machine learning skills are valuable today and will only become even more so in the future.

Fill the Skill Gap

Data science technologies, including machine learning, will create millions of jobs over the next several years. However, the current labor shortage in the U.S. is part of a massive challenge: there is a wide gap between the skills available among current workers and those needed on a worldwide basis.

Consider some of the following tools and skills which benefit not only Machine Learning Engineers but also Data Scientists and other data-driven professions:

  • Data Visualization - Data visualization tools like Power BI and Tableau are essential for thousands of professionals in today’s job market. A quick online job search reveals titles like Data Visualization Engineer, Analytics Specialist/Data Wrangling, and Tableau Developer.
  • These essential software programs require formal training, and many job listings specify one or both as requirements in the description. For many data-centered pros, the best way to learn Tableau or Power BI is through a broader curriculum like Noble Desktop’s Data Analytics Technologies Bootcamp or the Tableau Bootcamp offered as part of their Data Analytics Certificate program.
  • SQL - Structured Query Language, or SQL, isn’t just an arcane database skill for Machine Learning Engineers. Professionals as varied as Full Stack Developers, Marketing Data Analysts, and Software Engineers need to be comfortable reading and writing SQL queries.
  • Python - While Python programming might not be the first thing you think of when you read the phrase ‘machine learning,’ its importance to ML is surprisingly high. Noble Desktop’s Python Data Science & Machine Learning Bootcamp emphasizes both in a beginner-friendly program that includes 1-on-1 mentoring sessions. Whether you plan to be a Machine Learning Engineer or a Data Scientist, this course can put you on the path.

Stabilize Your Future

Machine learning jobs are on the rise. According to Glassdoor, Machine Learning Engineer ranked among the Top 10 Best Jobs in America for 2022. But it doesn’t stop there: related positions comprise most of the list, including roles like Data Scientist, Data Engineer, and Enterprise Architect. The need for targeted data collection and analysis has never been greater. 

Numerous industries today stand in dire need of more workers. The labor shortage has led directly to an increase in the use of artificial intelligence, especially machine learning. Companies now use ML to improve employee retention and determine the best options for remote workers to be productive. In addition, ML algorithms can help employers relieve workers of repetitive tasks, particularly in accounting and routine customer service inquiries, and even eliminate pesky problems like spam emails at work.

Stabilizing your future in a world dominated by machine learning algorithms might sound tricky, but it doesn’t have to be. Consider training for a position with a title like one of the following:

  • Data Scientist
  • Natural Language Processing (NLP) Scientist
  • Database Engineer
  • Machine Learning Engineer
  • Artificial Intelligence (AI) Engineer
  • Data Architect
  • Business Intelligence (BI) Analyst
  • Cybersecurity Analyst
  • Robotics Engineer

With so many specializations, it may seem overwhelming. However, once you start learning ML algorithms, you’ll soon see how many opportunities require skills in this exciting field.

How to Start Learning Machine Learning

Students seeking a machine learning education benefit most from live training, either in-person classes or those held live online via teleconferencing. Live classes keep participants engaged, and they often gain from networking with fellow attendees. Bootcamps and certificate programs, like the Python Machine Learning Bootcamp from Noble Desktop, offer immersive training in a dynamic learning environment.

For those who are not yet ready to commit to a comprehensive training program, an excellent starting point is on-demand or self-paced machine learning training. Topics include programming languages like Python and R, Microsoft Azure, and open-source libraries like TensorFlow and PyTorch. While you won’t have an instructor holding you accountable for assignments, you can benefit from learning on your schedule at any hour. Some on-demand courses are either free or available through a platform subscription plan.

Are you entirely new to machine learning? Check out a few free online resources, like those available through the Noble Desktop Learn Hub. You’ll find blog posts, tutorials, and articles relevant to machine learning, including data analytics, data visualization, and Python programming. You’ll find a wealth of video seminars on the Noble site featuring related topics like SQL and Python, among others.

Read the full guide on how to learn machine learning.

Learn Machine Learning with Hands-on Training at Noble Desktop

Noble Desktop offers a variety of bootcamps and certificates that feature machine learning, both in-person and live online via teleconferencing. Some include Python as a focus, others include machine learning as part of a broader data science curriculum, and others cover ML in a FinTech curriculum. All bootcamps and certificate programs feature small class sizes to maximize personal attention from expert instructors.

  • Data Science Certificate - Noble’s Data Science Certificate program teaches participants data science fundamentals before advancing through machine learning, Python for automation, and Structured Query Language (SQL). This immersive certificate is open to beginners; you can retake it for up to one year at no additional charge.
  • Python Machine Learning Bootcamp - Programmers already comfortable with Python and its data science libraries can get their machine learning training as part of the Python Machine Learning Bootcamp. Attendees can save by taking this shorter course as part of the Data Science Certificate program.
  • Python Data Science & Machine Learning Bootcamp - This comprehensive bootcamp combines the same ML and Python training modules as the Data Science Certificate but does not include the Structured Query Language (SQL) bootcamp. It’s open to beginners and designed to prepare students for entry-level Python engineering or data science positions. 

For more information on Noble Desktop’s data science classes, including machine learning, check out all their full-time and part-time data science programs.

Key Insights

  • Machine learning (ML) is one of the hottest fields in today’s business world.
  • Top ML positions include:
    • Machine Learning Engineer
    • Data Scientist
    • Data Architect
    • Artificial Intelligence (AI) Engineer
    • Cybersecurity Analyst
  • Machine learning algorithms dominate the internet, and more and more businesses use them to streamline processes, determine customer journeys, and even improve employee job satisfaction. 
  • Top industries using machine learning technology the most include:
    • Banking, Financial Services & Insurance (BFSI)
    • Retail
    • Health & Wellness
    • Information Technology (IT) Services
    • Cybersecurity
  • ML also plays a significant role in computational linguistics, NLP science, and design.
  • Top skills for machine learning professionals include Python, SQL, Tableau, and Power BI.
  • Immersive bootcamps and certificate programs offer an excellent method of studying machine learning tools and skills in a concentrated timeframe. You can receive comprehensive machine learning training through an in-person or live online course with Noble Desktop.

How to Learn Machine Learning

Master machine learning with hands-on training. Use Python to make, modify, and test your own machine learning models.

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