The Top 10 Algorithms For Machine Learning That Every Data Scientist Should Understand

This blog covers the top 10 machine learning algorithms that every data scientist should be familiar with. Linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, naive Bayes, gradient boosting, deep learning, and reinforcement learning are the algorithms discussed. The advantages and disadvantages of each algorithm are discussed, as well as examples of how they have been used. After reading this blog, data scientists should have a better understanding of the most significant machine learning algorithms and be prepared to take on a variety of data science projects.