8 Algorithms to Build Machine Learning Products

Simon Schreiber
The Startup
Published in
6 min readJun 29, 2020

Machine learning is a discipline of AI. It uses statistics to solve user problems. So, if you want to develop a machine learning product you need to understand the underlying algorithms. And surprise: statistics. This article is a non-technical overview of 8 proven algorithms to use in Machine Learning products.



Generally speaking, an algorithm is a procedure in the form of program code. An algorithm is used to solve a problem using a proven procedure [1]. For illustration purposes, all algorithms [2] are explained below with examples from the online real estate market.

(Learning) Decision Trees

Decision trees are a popular method of decision making. In practice they are often used due to their good traceability. The result obtained can be checked by the user at any time since a decision tree consists of a hierarchical sequence of decision rules. In practical use, the decision tree takes values ​​and returns a decision. Conceptually, a distinction must be made between classification trees (predict a label) and regression trees (predict a quantity).

A decision tree is useful for:

An application on the real estate market is the decision to be made as to whether it is more advantageous for a private person to rent or buy a property:

Learning decision trees are a special form of decision trees. A decision tree is learning if it is able to increase its efficiency. The learning decision tree uses training data and recognizes whether it is necessary to check all paths or not. As a result, the tree becomes shallow and the calculation faster.

Random Forest

The Random Forest algorithm is one of the most popular algorithms for solving classification problems. Classifying means assigning a new data point to an existing category.