Machine learning algorithms

The foundation of machine learning is its algorithms. They are complex sets of rules and mathematical models that train machines to analyze information, draw conclusions, and make informed decisions.

Machine learning algorithms are heavily used for predictive modeling, which plays an important role in a multitude of applications ranging from personalizing recommendations on streaming platforms to predicting stock market trends.

Machine learning algorithms can be categorized as follows

Supervised learning

Algorithms are trained on pre-labeled training data and extrapolate this learning to predict outcomes for unseen data. The mechanism is similar to a teacher-student relationship, where the algorithm (student) takes knowledge from the teacher (data) and applies it to new scenarios.

Unsupervised learning

Algorithms work with unlabeled data, discovering patterns and structures on their own. This can be compared to an explorer who navigates through unknown territory without any prior knowledge, only to familiarize himself with the terrain and make discoveries in the process.

Reinforcement learning

Algorithms learn from the results of their actions, essentially through their trial and error process. They perform actions in a particular environment in order to maximize a reward signal. Transferring this to everyday life, “reinforcement learning” can be compared to teaching a dog to give a paw.