It takes time to master machine learning (ML) fundamentals. While some machine learning newbies begin with a plan for a specific career, most learners require a formal training program. Getting the best training involves considering your schedule, budget, and learning style.

Different students try different types of training. The main types include bootcamps and certificate programs, self-paced or on-demand courses, and webinars or tutorials. Read on for more about the various options and how long they take.

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.

Benefits of Learning Machine Learning

Machine learning programs have become so common that you interact with them daily. Consider Amazon’s “Compare Similar Items” feature, recommendations lists on Netflix, and Reels suggestions on Instagram. All these tools come from machine learning algorithms.

If you plan a career in data science or analytics, ML can be a core segment of your education. Data Scientists and Data Engineers should be familiar with ML concepts. A Machine Learning Engineer or Machine Learning Architect has specialized skills like deep learning, data modeling, and natural language processing (NLP).

Read more about why you should learn machine learning.

How Long Do Machine Learning Courses Last?

While some resources recommend learning ML fundamentals in a short webinar or tutorial, others suggest taking an immersive bootcamp or certificate to master machine learning. How many weeks or months you need depends on multiple factors: your title, existing skillset, and availability are just a few.

The average ML curriculum lasts about six months, but you can spend years mastering all the skills needed for a career. Some people add ML fundamentals to an education that includes statistics, computer programming, and data science. Others learn ML as part of a broader curriculum in another discipline. So “how long it takes” truly depends on these factors.

Different Courses with Different Schedules

With the many training options available, deciding which one to try can be challenging. The delivery method and depth of instruction will affect how long a course takes. 

Live online or in-person bootcamps usually run from 50 to 75 hours, though some run over 100. If you take a full-time class, you may finish in a few weeks, whereas a part-time certificate can take months to complete.

In-Person & Online Classes

Among the most popular machine learning training methods are bootcamps and certificate programs. Some course providers offer in-person training, others live online via teleconferencing, and some even offer both options. 

Many machine learning courses are part of broader data science or data analysis curricula. For example, Noble Desktop offers a Python Machine Learning Bootcamp as a 30-hour program. However, participants can save if they take this course as part of the 96-hour Python Data Science & Machine Learning Bootcamp or 114-hour Data Science Certificate.

In-person and online classes are where the difference between those who need a few tools and those who need in-depth training is most apparent. Whereas an Administrative Assistant might not require training beyond an email manager like Sanebox or a text-speech generator like Murf, ML pros need expertise in Python libraries, algorithms, and data visualization. Machine learning training requires a serious commitment of time and energy.

Synchronous & Asynchronous Classes

Synchronous learning takes place live online or in person, whereas asynchronous learning allows students to access materials at different times from varying locations. 

Asynchronous learning is also sometimes called self-paced or on-demand. This method has a built-in set of advantages and disadvantages. While course providers schedule live classes over a few weeks or months, asynchronous learners can take much longer to study their materials. However, most providers expect course completion within a particular time frame, such as six months or less.

Some asynchronous courses take much longer than others. For example, Udemy’s Machine Learning & Deep Learning in Python & R includes 33 hours of video, four articles, and five additional learning resources. By contrast, Coursera offers a course called Build a Machine Learning Web App with Streamlit and Python that takes less than two hours.

Career-Focused & Skills-Focused Classes

Career-focused and skills-focused classes differ in significant ways, too. Whereas a skills-focused course usually centers on one or more skills, a career-focused program typically includes multiple skills-focused modules, like a data analytics or software engineering program.

Career-focused courses will usually run longer than skills-based courses. For example, the Python Machine Learning Bootcamp from Noble Desktop is a 30-hour program for experienced Python pros who want to improve their ML skills. Those who want to learn ML as part of a broader data science curriculum can take the Python ML Bootcamp as part of Noble’s Data Science Certificate.

Introductory Classes & Immersive Courses 

As a rule, shorter classes introduce students to machine learning or teach them an advanced skill or tool. Longer courses typically teach an advanced set of skills. However, many course providers combine multiple introductory, intermediate, and advanced courses into one long training program.

Such is the case with the Data Science and Data Analytics Certificate programs from Noble Desktop. Each includes the 30-hour Python Machine Learning Bootcamp but with a different emphasis. Because each certificate is an immersive program, neither features a short introductory class.

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 AI, or artificial intelligence
• Top ML careers include:
• Machine Learning Engineer
• Data Scientist
• Business Intelligence (BI) Analyst
• Factors influencing how long it takes to learn ML include your skillset, availability, and current position.
• In-person ML programs run from 30-hour bootcamps to certificate programs that take 100+ hours.
• Self-paced or asynchronous ML classes can take anywhere from an hour to 30+ hours.
• Career-focused courses typically take longer than skills-focused courses. Many career-focused programs consist of multiple skills-focused modules.
• You can receive comprehensive machine learning training through Noble Desktop, either in person or online. Popular courses include:
• Python Machine Learning Bootcamp
• Python Data Science & Machine Learning Bootcamp
• Data Science Certificate
• Data Analytics Certificate