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.
What is machine learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that enables 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:
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.
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.
Why should you learn machine learning?
- 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.
Featured Machine Learning courses and programs
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What do you learn in an ML course or 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 diving into 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.
While Python is the industry standard for building ML models, many of the high-performance backend libraries and frameworks that power these models are built using low-level languages like:
- C
- C++
- Rust
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 you reach the end of your program, you may engage with advanced ML concepts, such as:
- Bayesian ML modeling
- Stochastic optimization methods
- Domain-specific ML applications (such as big data, healthcare, or robotics engineering)
Programs differ, but they are generally structured to provide a logical path for building complex knowledge and skills.
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How do you get started in machine learning?
1. Shift your mindset
Consider 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. Take a course
Explore online courses to better understand how machine learning works and its real-world applications. Online courses can prepare you for a career in developing ML models or using them to enhance your workflow. On edX, you can find courses for any stage of ML learning:
- MITx: Machine Learning with Python: Part of the edX MicroMasters® Program in Statistics and Data from MIT, this foundational course covers the field, models, and use cases.
- HarvardX: MLOps for Scaling TinyML: In this course, you'll learn about tiny machine learning to create models at scale.
- MGH Institute: Introduction to AI & Machine Learning in Healthcare: Build specialized machine learning skills for healthcare in this online edX course.
Not sure if you're ready to commit to a course? Peruse course materials and requirements by auditing a course on edX.
3. Solve a problem
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 in 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.
4. Experiment with 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.
- 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.
- Microsoft Azure: Microsoft Azure can help you train and deploy AI models. Other benefits include support for different programming languages and scalable cloud storage.
- TensorFlow: An open-source alternative to Microsoft Azure and IBM Watson, TensorFlow offers an extensive software library and support documentation.
5. 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.
- 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 much time 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 1-12 weeks to complete. Individual courses can help you learn specific knowledge and skills. Prices range from $20-$300.
Courses
A machine learning course takes 1-12 weeks to complete. Individual courses can help you learn specific knowledge and skills. Prices range from $20-$300.
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