Machine learning is a branch of Artificial Intelligence and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
There are three major types of machine learning:
- Supervised ML: Teach the algorithm by examples. If the input is X then the output should be y (Target Variable). Some popular algorithms for this are linear regression, logistic regression, decision trees, random forests, SVM, naive Bayes, XGboost, AdaBoost, etc.
- Unsupervised ML: No Target variable is present. Basically you DON’T have data which says the input is X and output is y. In this scenario, important patterns can be derived directly from data like grouping the similar type of rows (Clustering) without any prior knowledge of the given data. Some popular algorithms are K-Means, DBSCAN, PCA, ICA, Apriori.
- Reinforcement ML: When the Machine Learning algorithm learns by its mistakes and improvises in the next iteration in order to achieve an objective. Some popular algorithms are Monte Carlo, Q-Learning, SARSA.