Simply put, an algorithm is a general set of instructions created to solve a specific type of problem. It is considered to be accurate when it produces the correct output for each problem instance. Most algorithms are built to either understand data within the context of a particular question via supervised learning, or to understand big data with the help of AI based on unlabeled data, via unsupervised learning. Let’s specify some of the used terms to make this definition clearer:
The typical data scientist has only one idea in mind while using algorithms - discovering links in his data (also known as patterns). In the context of using data science methods, we always assume that there are hidden links in the data and that our algorithms will help us find them. However, you need to keep in mind that this is a strong assumption because there is usually no link in data by default. This may seem surprising, because we tend to see it everywhere!
Support Vector Machine (Regression or classification); Gradient Boosting (Regression or classification); Random Forest ( Regression or classification); Polynomial Regression ( Regression)
Logistic Regression : Spam Detection (Predicting if an email is Spam or not ); Decision Tree: Churn prediction; Random Forest: Classification of customer tastes
Additional Resources:
