Pinned Repositories
Dataset
Datasets that are used for Machine Learning and Deep Learning
Decision-Tree-Classification
Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables. Decision tree classification observes features of an object and trains a model in the structure of a tree to predict the class of the data.
Decision-Tree-Regression
Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.Decision tree regression observes features of an object and trains a model in the structure of a tree to predict data in the future to produce meaningful continuous output. Continuous output means that the output/result is not discrete, i.e., it is not represented just by a discrete, known set of numbers or values.
Linear-Regression
Linear Regression Algorithms for Machine Learning using Scikit Learn
Logistic-Regression
Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either 0 (for not spam) or 1 (for spam).
Naive-Bayes-Classification
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.
Neural-Network
Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) implementation on different datasets for classification in Machine Learning.
Polynomial-Regression
Polynomial regression is a special case of linear regression where we fit a polynomial equation on the data with a curvilinear relationship between the target variable and the independent variables.
Random-Forest-Classification
Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.
Searching-Algorithms
Searching Algorithms using Python.
girirajv10's Repositories
girirajv10/Decision-Tree-Regression
Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.Decision tree regression observes features of an object and trains a model in the structure of a tree to predict data in the future to produce meaningful continuous output. Continuous output means that the output/result is not discrete, i.e., it is not represented just by a discrete, known set of numbers or values.
girirajv10/Linear-Regression
Linear Regression Algorithms for Machine Learning using Scikit Learn
girirajv10/Dataset
Datasets that are used for Machine Learning and Deep Learning
girirajv10/Decision-Tree-Classification
Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables. Decision tree classification observes features of an object and trains a model in the structure of a tree to predict the class of the data.
girirajv10/Logistic-Regression
Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either 0 (for not spam) or 1 (for spam).
girirajv10/Naive-Bayes-Classification
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.
girirajv10/Neural-Network
Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) implementation on different datasets for classification in Machine Learning.
girirajv10/Polynomial-Regression
Polynomial regression is a special case of linear regression where we fit a polynomial equation on the data with a curvilinear relationship between the target variable and the independent variables.
girirajv10/Random-Forest-Classification
Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.
girirajv10/Searching-Algorithms
Searching Algorithms using Python.
girirajv10/Sorting-Algorithms
girirajv10/Support-Vector-Machine
Support Vector Machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification, implicitly mapping their inputs into high-dimensional feature spaces.
girirajv10/machine_learning_examples
A collection of machine learning examples and tutorials.