Explore machine learning courses online
What is machine learning?
Machine learning is an area of artificial intelligence and computer science that comprises supervised and unsupervised learning and includes the development of software and algorithms that can make predictions based on data.Footnote 1 Machine learning is utilized across various industries. In the field of data analytics, for example, machine learning is used to make predictions based on trends and insights from data.
A prime example of the application of machine learning is the autonomous vehicle. Sensors around the vehicle deliver thousands of data points which are analyzed and processed to move the vehicle toward its destination. Collective data from thousands of self-driving cars can be used to improve vehicle safety and prevent accidents.
There are many different career paths within the machine learning field that can lead to a fulfilling career. Machine learning online courses and machine learning certifications are two ways that individuals can develop the skills to compete for jobs in this field.
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Types of machine learning
Machine learning and artificial intelligence can sometimes be conflated with one another. In reality, they serve similar purposes under the same data science umbrella.Footnote 2 Looking for a precise machine learning definition? Machine learning is a component of artificial intelligence that uses statistical methods to train algorithms to uncover patterns or insights that can be used to inform decisions. To better understand machine learning, it’s important know the three types of machine learning:
Supervised machine learning uses datasets to train algorithms to make predictions. The algorithm predicts an outcome based on mapping inputs to desired outputs, adjusting the weights until the data is accurate. Examples of supervised machine learning include spam detection, text classification and recommendation systems.Footnote 3
Unsupervised machine learning uses algorithms to uncover hidden patterns, data classification and other data insights. It does not have a target outcome but rather is used for sorting populations and demographics into different groups. Unsupervised machine learning can be used to develop image recognition and customer segmentation tools.Footnote 4
Reinforcement machine learning is a behavioral algorithm that learns in real time through trial and error how to find accuracy rather than being trained on a specific outcome. In this model, machine learning uses past experiences to better inform decisions, such as determining the best recommendation for an unresolved problem.Footnote 5
Machine learning course curriculum
There are a number of machine learning online courses that teach the basics of machine learning, cover the differences between machine learning versus artificial intelligence, and help map out the steps for how to become a machine learning engineer.
A typical machine learning course could explore the following topics:
A machine learning course might also delve deeper into the real-life applications of these topics, such as spam filtering, credit card fraud detection, facial recognition and handwriting recognition.
Discover machine learning jobs
Knowledge of machine learning can come in handy in many data- and AI-related jobs. Here are a few examples:
Big data engineer
Having relevant skills and experience in the field of machine learning may help set individuals on the path to an exciting career. Take an online machine learning course and explore other AI, data science, predictive analytics and programming courses to get started.
How to become a machine learning engineer
A variety of learning pathways exist for individuals interested in pursuing machine learning jobs, including a boot camp, or by obtaining a bachelor’s degree in computer or data science. There’s also the option of pursuing a bachelor’s degree in a related field.
To become a machine learning engineer, it may be helpful to take a machine learning course online or in-person. Some machine learning engineer jobs as well as those in computer and information research science may require a master’s degree in computer science, computer engineering, math, statistics or a related subject.Footnote 6 Advanced math skills may prove useful along this career path, so consider taking courses on linear algebra and group theory. Those who pursue a machine learning education may also take courses about data modeling, data architecture, machine learning frameworks, and programming languages such as Python that can help with building web applications.