Machine Learning

Reinforcement learning

Reinforcement Learning in Machine Learning

Reinforcement learning (RL) is a type of Machine Learning associated with how intelligent agents should take actions in an environment to maximize rewards. It is employed by finding the best possible path or behavior it should make in a specific situation. The agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves …

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Decision tree Algorithm

Decision tree algorithm is a commonly used supervised learning algorithm, solves classification problems. Decision tree is the graphical representation of all the possible solutions to a decision. Decisions are based on some conditions. Decision tree is a tree-like structure, that breaks down a dataset into smaller and smaller subsets. The final result is a tree with decision …

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KNN Algorithm (K-Nearest Neighbors)

K-Nearest Neighbors(KNN) is one of the simplest supervised machine learning algorithms used for classification.  KNN algorithm performs by matching the test data with K neighbor training examples and decides its group. K is the number of neighbors in KNN. The KNN algorithm is used in the following scenarios: Data is noise-free Data is labeled Dataset …

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Linear Regression

Linear Regression using sklearn

Simple Linear Regression using scikit-learn Linear Regression is a statistical model used to predict the linear relationship between two or more variables. Here we are going to demonstrate the linear Regression model using the Scikit-learn library in Python. Scikit-learn also defined as sklearn is a python library with a lot of efficient tools for machine learning …

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What is Linear Regression?

LINEAR REGRESSION: Linear regression is a Supervised Machine Learning algorithm. It is a statistical model used to predict the linear relationship between two or more variables. Mostly we treat datasets with quantitative values as regression models. Linear regression models use a straight line, logistic and nonlinear regression models use a curved line. There are two …

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