Revisiting the Traditional Algorithms

We all know the types of Machine Learning, but let's take a moment to refresh our memory.

Types:

• Supervised
• Unsupervised
• Semi-supervised
• Reinforcement

Today, we're going to discuss the supervised type, which deals with labeled data.

1] Linear Regression: It mainly focuses on continuous data.

2] Logistic Regression: Deals with binary classification tasks, e.g., predicting yes/no (or) 0/1.

3] Decision Trees: A tree-based model that learns simple decision rules inferred from the data features.

4] Random Forests: Combines multiple trees to form a forest. Random forests are used for both classification and regression.

5] Support Vector Machines (SVM): Effective in high-dimensional spaces, SVM is primarily used for classification but can also be used for regression.

6] K-Nearest Neighbor: Estimates how likely a data point is to be a member of one group or another.

These algorithms are covered in simple terms to aid understanding. Next month, we’ll continue with the other types in our newsletter.

If you have any points to add, please share them in the comments. We all love to learn and improve our skills professionally.

Thank you. We will meet again next month.

#DataScience #MachineLearning #Supervised #Team