Supervised, Unsupervised and Semi-supervised Learning Algorithm - Machine Learning Tutorial-02

This video demonstrates Supervised Learning, Unsupervised Learning and Semi-supervised Machine Learning Algorithm. Welcome to Introduction to Machine Learning - Machine Learning Tutorial.

Supervised Learning: In supervised learning, label data or output or supervisor data or teacher data is given in training data. Supervised learning must contain label data during training phase.
Supervised Learning Algorithms are:
- Nearest Neighbor (NN)
- K-Nearest Neighbor (KNN)
- Artificial Neural Network (ANN)
- Support Vector Machine (SVM)
- Decision Tree (DT) ex: ID3, C4.5
- Apriori Algorithm
- Hidden Markov Model (HMM)
- Logistic Regression
- Naive Bayes Classifier

Unsupervised Learning: In unsupervised learning only input data is given in training data. No label data or supervisor data is given. Unsupervised Learning separate data into clusters.

Un-Supervised Learning Algorithms are:
- K-Means Clustering
- Modified K-Means Clustering
- Hierarchical Clustering
- SOM (Self Organizing Map)
- Fuzzy C-Mean Clustering

Semi-Supervised Learning: In semi-supervised learning for some data label data is given and for some data label data is missing. Input data that are not labelled have to be labelled using labelled input training data. Then apply testing data for intelligent decision.

Please watch till the end of the video to get a clear concept of machine learning algorithms, I hope you will enjoy it.