Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector for Regression (SVR)
Decision Tree Regression
Random Forest Regression
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Hierarchical Clustering
K-means Clustering
Apriori
Eclat
Upper Confidence Bound
Thompson Sampling
Artificial-Neural-Networks-(ANN)
Convolutional-Neural-Networks-(CNN)
Kernel-PCA
Linear-Discriminant-Analysis
Principal-Component-Analysis
Model Selection
XGBoost