# Essentials of Machine Learning Algorithms

The state-of-the-art Machine Learning (ML) algorithms could be classified into:

- Supervised Machine Learning - The dataset used in Supervised Learning is labeled which means for each row there is a target variable given. The model is trained with the supervised training set and then tested on the unknown data. Linear Regression, Logistic Regression, etc., are some Supervised Machine Learning algorithm.
- Un-Supervised Learning - Unlike supervised learning, Un-Supervised Machine Learning algorithm, the dataset is unlabelled and needs to be grouped together based on the similarity among the data points. K-Means clustering, Apriori are some of the algorithms used for clustering the data points into different groups.
- Reinforcement Learning - A special type of Machine Learning where the model learns from past actions and it is rewarded for every correct move and penalized for any wrong move taken. Google’s AlphaGo is an example of a Reinforcement Learning application.

### -** List of Common ML Algorithms**

Machine Learning is an innovative and important field in the industry. The type of algorithm we choose for our ML program changes depending on what we want to accomplish. Here is the list of commonly used ML algorithms. These algorithms can be applied to almost any data problem:

- Linear Regression
- Logistic Regression
- Linear Discriminant Analysis
- Classification and Regression Trees
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Learning Vector Quantization (LVQ)
- Support Vector Machines (SVM)
- Random Forest
- Boosting
- AdaBoost

**[More to come ...]**