How to Use Low-Code to Enable Machine Learning
Learn how low-code simplifies machine learning application development.
Machine learning is a subset of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed. The process involves feeding vast amounts of data into models and creating algorithms that allow them to recognize patterns, make decisions, and continuously improve their performance.
Machine learning relies on three fundamental components:
Machine Learning is a method that enables computer systems can acquire knowledge from experience. It involves training algorithms using historical data to make predictions or decisions without being explicitly programmed.
Various types of machine learning can be used for different purposes.
Supervised machine learning provides the algorithm with labeled data, meaning each data point is associated with a known output. The algorithm learns by comparing its predictions with the correct outputs and adjusting its parameters to minimize errors. This type of learning is used for tasks like classification and regression.
In unsupervised machine learning, the algorithm must find patterns and relationships in unlabeled data independently. Clustering and dimensionality reduction are common applications of unsupervised learning.
Semi-supervised learning combines elements of supervised and unsupervised learning. It involves training the algorithm on a limited amount of labeled data and a more extensive amount of unlabeled data. This approach is useful when obtaining labeled data is expensive or time-consuming.
The primary difference between supervised and unsupervised learning lies in the presence of labeled data. Supervised learning requires labeled data for training, while unsupervised learning does not. Supervised learning is used for tasks with clearly defined outputs, while unsupervised learning is suitable for exploring unknown patterns in data.
Machine learning algorithms form the backbone of the entire process. Here are some of the most common ones:
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Understanding the different types and algorithms of machine learning is essential to unlocking its full potential in your applications. OutSystems makes that easier by providing connectors to machine learning services that revolutionize how your customers interact with technology and make decisions. As a result, the future of low-code application development is even more promising, offering endless possibilities to create intelligent and transformative solutions. Embrace the power of machine learning and stay ahead in the digital era with OutSystems.