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Machine learning online courses and programs

Machine learning can play a key role in process management, business efficiency, and personalization. Learn about this fast-evolving subdomain of artificial intelligence technology.

Computer systems compile enormous quantities of data. Machine learning can unlock insights and opportunities in that data by applying purpose-built tools that automatically respond and adjust to data inputs.

By becoming skilled in machine learning, you can position yourself for success in an evolving labor market. Discover opportunities as you familiarize yourself with this important artificial intelligence subfield.

What is machine learning?

Machine learning uses computer algorithms to analyze data and make decisions based on those analyses. Algorithms are coded, step-based instructions that computer systems use to perform tasks or solve problems. The algorithms used in machine learning are programmed to adjust their performance as they are exposed to more training data, allowing them to "learn."

The following definitions and examples provide more specific insights into the differences between machine learning and artificial intelligence:

Machine learning

This branch of artificial intelligence concentrates on developing computer systems that can learn independently. Machine learning programs can produce content that meets users' needs by analyzing large datasets.

For example, in 2022, researchers launched a project designed to predict injury risk in soccer players. Drawing exclusively on historical data, the project used time-series analysis and predictive modeling to classify players' injury risk based solely on their acute and chronic physical loads.

In this case, the algorithms did not adapt their behavior to external user inputs the way artificial intelligence systems do. Instead, the project focused solely on a narrow, domain-specific application that generated more precise results as training data quantities increased.

Artificial intelligence

AI is a branch of computer science that creates computer systems that mimic or exceed human intelligence. Many consumer electronics contain primitive AI that can help people with simple tasks.

In 2021, researchers proposed a rules-based AI model for playing a popular card game known as Big Two. The model used six primary rules and 29 sub-rules for making gameplay decisions. It did not consider the results of past game actions — a feature that, by definition, removed the model from the domain of machine learning.

Benefits of learning ML

  • Enhance your career prospects as businesses make deep investments in machine learning technologies.
  • Apply your ML knowledge to strategically important fields like cybersecurity and fraud prevention, increasing your impact.
  • Help employers automate labor-intensive tasks, improve their operating efficiency, enhance customer experiences, and cut costs.
  • Gain valuable personal insights into how ML technologies function as they increasingly interact with your daily life.

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What will you learn in a machine learning program?

The topics and concepts covered in ML courses and programs differ at various levels. Introductory courses focus on fundamentals like defining differences between AI, ML, and automation.

Intermediate and advanced courses build on those foundations by introducing and exploring increasingly complex and technical content.

Notably, machine learning courses may emphasize relevant programming languages. Consider taking an introduction to Python course before starting on machine learning. With Python, you can analyze linear models and neural networks, choose the best models for the applications, and run a predictive or classification project from start to finish.

ML and AI also draw extensively on mathematics, including algebra, calculus, probability, and statistics. Building math skills in these domains can also help — especially as you move from basic into intermediate and more advanced academic content.

To that end, you will likely encounter these intermediate topics as you move through a concentrated program in machine learning:

  • Computational complexity theory
  • Algorithmic engineering
  • Pattern recognition and behavioral imaging
  • Deep learning

As your program culminates, you may engage with such advanced ML concepts as:

  • Bayesian ML modeling
  • Stochastic optimization methods
  • Domain-specific ML applications (such as big data, healthcare, or robotics engineering)

Programs differ but are generally structured to provide a logical path to building complex knowledge and skills.

What workers say about AI and machine learning

64%
of workers are at least somewhat likely to pursue AI-related education or training in the next six months
31%
identified machine learning as the top technical skill to advance their career
54%
agree that AI and machine learning skills are very important or extremely important for staying competitive in their careers

Source: Economic pressures propel workers to reskill, AI anxiety drives surge in upskilling among workers

How to get started in machine learning

1. Shift your mindset to work with machine learning

Considering ML's potential to upend the workforce. If you view machine learning as a thought partner rather than a rival, you can explore how this emerging technology can benefit you.

Before starting a course, experiment with popular platforms like ChatGPT. Analyzing its responses can teach you about the technology's benefits and drawbacks.

2. Pick a problem to solve

After completing at least one machine learning course, consider how it can solve a problem in your professional or personal life.

In the workplace, machine learning can help you with routine tasks, such as organizing data on a spreadsheet or identifying potential customers. Focus on a problem that matches your current ability level.

Try to stay motivated if you feel overwhelmed or don't get the results you want on the first try. Learning anything new takes time. You may need further practice with machine learning tools.

3. Experiment with machine learning tools

Machine learning tools can make your educational journey much simpler. Explore popular tools that can help you master machine learning fundamentals and intermediate concepts. Prices are current as of June 2025.

  • IBM Watson: With IBM Watson, you can analyze data, create machine learning software with natural language processing, and use applications to custom-tailor programs to your industry. Although you can try out IBM Watson for free, the Plus plan featuring generative AI capabilities costs $140 per month.
  • Microsoft Azure: Microsoft Azure can help you train and deploy AI models. Other benefits include support for different programming languages and scalable cloud storage. Azure Machine Learning costs $70-$2,242 per month, depending on the plan.
  • TensorFlow: An open-source alternative to Microsoft Azure and IBM Watson, TensorFlow offers an extensive software library and support documentation. TensorFlow does not charge a subscription fee, making it an ideal option for budget-conscious learners.

4. Build a portfolio

Intermediate and advanced machine learning courses can help you build an online professional portfolio. Showcase projects highlighting your analytical skills and accomplishments. Add relevant commentary to each entry so hiring managers without computer science backgrounds can understand your work.

Make sure to include a link to your portfolio on your résumé and practice discussing its content before job interviews.

How to choose an ML course or program

  • Current skill level: Begin your career journey by considering your current skill level. Explore introductory courses if you lack a computer science or STEM background.
  • Cost: Avoid debt wherever possible. Focus on free or low-cost introductory courses to learn the basics before committing to costlier certificate or degree programs.
  • Timeline: Calculate how long you need to reach your goals by determining the number of hours you can commit to your education each week. ML programs can take a few weeks to a few years.
  • Career goals: Decide whether you want to learn machine learning to protect your current job or advance your career. The former may require only a certificate, while the latter may require a degree.
  • Program outcomes: Review program outcomes closely when comparing courses of study. Good programs prepare students for career advancement or a new job.

Courses

A machine learning course takes 4-12 weeks. Individual courses can help you learn specific knowledge and skills. Prices range from $50-$300.



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    Frequently asked questions

    How can I start to learn machine learning?

    Start with an introductory machine learning course. This experience can help you decide what other courses, certificates, or degrees can help you reach your career goals.

    Is machine learning difficult to learn?

    Entering the machine learning field may require an undergraduate degree in computer science, math, data science, or engineering. STEM courses may be challenging if you do not enjoy these subjects. If you think you might have trouble learning machine learning, research courses, programs, and degrees that offer academic support.

    What are the four types of machine learning?

    The four types of machine learning are supervised, unsupervised, semisupervised, and reinforcement learning. Supervised learning algorithms learn by pairing labeled inputs with labeled outputs. Unsupervised learning algorithms that find patterns in data independent of any instructions or labeling. Semisupervised learning algorithms that use a fraction of the labeled data provided to supervised learning algorithms. Reinforcement learning algorithms that learn through trial and error.

    Can machine learning be self-taught?

    Yes, you can learn machine learning fundamentals on your own. However, you must still earn a university certificate, professional certification, or degree to qualify for most entry-level positions. Completing a formal educational pathway can also help you build your professional portfolio.

    Last updated September 30, 2025