Machine_Learning
Supervised ML Algorithms
Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Supervised learning is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y). In the real-world, supervised learning can be used for Risk Assessment, Image classification, Fraud Detection, spam filtering, etc.
| Algorithm Name | Link |
|---|---|
| Linear Regression | Click Here |
| Logistic Regression | Click Here |
| Decision Tree | Click Here |
| Random Forest | Click Here |
| K Nearest Algorithm (KNN) | Click Here |
| Bagging | Click Here |
| Adaboost | Click Here |
| Gradient Boost | Click Here |
| XGboost | Click Here |
| Stacking | Click Here |
Unsupervised ML Algorithms
Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. Some use cases for unsupervised learning — more specifically, clustering — include: Customer segmentation, or understanding different customer groups around which to build marketing or other business strategies. Genetics, for example clustering DNA patterns to analyze evolutionary biology.
| Algorithm Name | Link |
|---|---|
| KMeans | Click Here |
| Hierarchical Clustering | Click Here |
| DBSCAN | Click Here |
