What is Machine Learning?

Machine learning (ML) is one of the most important branches of artificial intelligence or AI. Voice assistants like Siri, product recommendations on popular websites, and even medical diagnostic tools rely on machine learning algorithms to provide information.

In this overview, you’ll learn more about what machine learning is, what it can do, who uses it, and how to learn it so you can determine how to add this skill to your professional toolbox. Whether you want to study ML as a primary discipline or as part of a broader AI curriculum, it makes sense to know more about ML tools and techniques.

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.

How Much Does Machine Learning Cost?

As a subset of artificial intelligence, machine learning isn’t something you download or buy. This complex discipline requires training in a variety of tools and skills. The ones you need depend on your current skill set, knowledge, and goals.

Top positions for those with ML training include:

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

Top sectors where ML skills are in demand include:

• Professional Services
• Banking, Financial Services & Insurance (BFSI)
• Information Technology (IT) Services
• Retail
• Manufacturing
• Agriculture
• Cybersecurity
• Transportation
• Health & Wellness

The most common uses for ML include sales lead development, service optimization, chatbots/digital assistants, and cybersecurity.

Depending on your field and position, you might need to learn programming languages like Python, databases like SQL Server and MySQL, and NLP skills. Fortunately, many of these are free, such as Python’s libraries and frameworks. The following should be on your radar:

• Pandas
• NumPy
• Matplotlib
• Anaconda
• Jupyter Notebook

Your most important investment in learning ML will be training. Courses like the Python Machine Learning Bootcamp or Python for Data Science Bootcamp from Noble Desktop typically cost around $1,500 - $2,000 and can be completed in months or even weeks.

What Are the Benefits of Learning Machine Learning?

Machine learning programs have become so common that you most likely interact with them daily. Consider Amazon’s “Compare Similar Items” feature, the “Because You Watched” recommendations on Netflix, and the recommended reels on your Instagram feed. All of these tools come from machine learning algorithms.

If you’re planning to start a career in data science or analytics, ML can be a core segment of your education. Anyone with a title like Data Scientist or Data Engineer should be familiar with machine learning concepts. A Machine Learning Engineer or Machine Learning Architect must have a specialized skill set in subjects like deep learning, data modeling, and natural language processing (NLP).

Read more about why you should learn machine learning. 

Machine Learning Careers

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.

How to Learn 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.

A Brief History of Machine Learning

Despite its growing popularity, most experts still consider machine learning a subcategory of artificial intelligence (AI). Also known as ML, machine learning as a model dates back to the 1940s when psychologist Donald Hebb developed theories based on communication between neurons in the brain. Ultimately, so-called Hebbian theories developed into propositions based on artificial neural networks (ANNs) or computing systems based on biological brain functions.

By the 1960s, the success of the first neuro-computer paved the way for more developments in neural network research. However, the discipline of machine learning focuses primarily on problem-solving rather than products or services. It was not until the internet became popularized in the 1990s that machine learning grew into a discipline with broad applications to data-based industries of all types.

In the 21st century, the influence of machine learning has grown exponentially. Massive organizations like Google and Facebook have used ML algorithms successfully in facial and speech recognition programs, and the technology has expanded to virtually every multinational tech company. Machine learning algorithms work behind the scenes when you listen to songs on Apple Music or Spotify, watch shows on Netflix or Amazon Prime, or react to a video on Instagram or TikTok.

The potential of this broad-ranging field seems almost limitless. As ML models become more intuitive, companies will better use them to personalize customer experiences. Sales data analysis, fraud detection, and even management decisions can now increasingly be influenced or even accomplished with the help of machine learning algorithms. With ML, the future is already here.

Comparable Fields

When comparing careers in machine learning (ML) to other disciplines, it’s critical to keep one thing in mind: machine learning is a subcategory of artificial intelligence (AI), a subcategory of computer science.

Specializations within the broad computer science field include:

• Artificial Intelligence (AI)
• Game Design
• Information Security
• Data Science
• Software Engineering

Of these—and there are others, like programming, graphics, and networks—software engineering may be the most interesting to compare and contrast with ML.

Machine learning professionals and Software Engineers have much in common. Both need to master computer programming languages like Python, along with its many libraries and frameworks. They typically have strong backgrounds in math, computer science, or both. And they generally need to work with software and databases.

However, there are many more differences than similarities. ML professionals are much more specialized than Software Engineers, who usually need to learn more programming languages and processes. Whereas Software Engineers may follow a process from initial concept through updating an application, algorithms rather than humans may accomplish much of a machine learning professional’s needs.

A combination of the skills of both these positions is that of the Machine Learning Engineer. These professionals must know ML as well as generalized programming skills. Machine Learning Engineer positions often have AI in the title, such as AI/ML Engineering Leader, AI & ML Services Engineer, or Lead Software Engineer (AI/ML Ops). When looking for more information on ML or software engineering careers, consider the many combinations and the qualifications and responsibilities these positions require.

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 most important subcategories of artificial intelligence (AI). Popular uses of ML in everyday activities include voice recognition assistants (Siri, Alexa), facial recognition technologies, product recommendations (“You May Also Like,” “Compare Similar Items,” “Because You Watched”), and medical diagnostic tools (X-ray, MRI).

• Machine learning as a model dates back to artificial neural networks (ANNs) theories developed by psychologist Donald Hebb. By the time the internet grew into common usage in the 1990s, ML had grown into a discipline with broad applications to virtually every industry.
• Top sectors where machine learning skills are most in demand include:
• Professional Services
• Banking, Financial Services & Insurance (BFSI)
• Information Technology (IT) Services
• Retail
• Manufacturing
• Agriculture
• Cybersecurity
• Transportation
• Health & Wellness
• Top positions for those with ML training include:
• Machine Learning Engineer
• Software Engineer
• Data Engineer
• Data Scientist
• Natural Language Processing (NLP) Scientist
• Machine Learning Designer
• Business Intelligence (BI) Developer
• Software Developer
• You can receive comprehensive machine learning training through an in-person or live online bootcamp or certificate program from Noble Desktop, like:
• Data Science Certificate
• Python Machine Learning Bootcamp
• Python Data Science & Machine Learning Bootcamp
  
  

For more details, including prerequisite information, visit Noble’s Data Science Classes page.