There are so many things you need to learn about machine learning algorithms, including their types and groupings, and the machine learning datasets accompanied by a brief explanation of some of the most popular machine learning algorithms that are often used by practitioners. A machine learning algorithm is an algorithm used in the machine learning process, where the system performs learning based on data. The types of machine learning algorithms can be grouped into supervised learning, unsupervised learning, and reinforcement learning. The selection of machine learning algorithms is based on the purpose or type of problem, computing resources, and the nature of the data, recommended reading.
Machine learning algorithms are algorithms used in the machine learning process, where the system performs learning based on data. Machine learning algorithms are applied in creating models, based on data sets. The more data, the algorithm will adjust to make the model work better. There are many groupings of algorithms in machine learning. Here are 3 types of various groups of machine learning algorithms, they are supervised learning, unsupervised learning, and reinforcement learning
1. Supervised learning
Supervised learning or supervised learning is a machine learning method that is determined based on the use of labeled data sets. In this dataset, there is a “label”, which is the column that is the target of the model output. In supervised learning, models are trained using a set of training data and trained under supervision to classify or predict outputs based on predefined labeled data.
2. Unsupervised learning
Unsupervised learning is a learning method using machine learning algorithms to analyze and classify unlabeled data sets. This algorithm finds hidden patterns in the data without the need for human intervention, so it is called unsupervised. Unsupervised learning is used to do:
– clustering
– association
– dimensionality reduction
3. Reinforcement learning
Reinforcement learning is a machine learning model similar to supervised learning, but the algorithm is not trained using sample data or training data. In reinforcement learning, this model learns as it goes by using trial and error.