This repository contains a collection of popular machine learning algorithms implemented in Python. It can be used as a resource for anyone interested in learning about or implementing machine learning models in their projects.
This repository contains a collection of popular machine learning algorithms implemented in Python.
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Naive Bayes
- K-Nearest Neighbors
- Support Vector Machines
Linear regression is a basic and commonly used type of predictive analysis. The goal of linear regression is to model the relationship between a dependent variable and one or more independent variables.
Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.
A decision tree is a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression.
In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Lesson from Machine Learning Certification