Machine learning is one of the buzzwords now being used in the IT industry. It is becoming more and more commonplace in scenarios like when Amazon proposes products that go well with what you’ve already purchased or when it suggests comparable movies after you view videos in a particular genre. These are just a few of the many examples that maximize its potential.
Many different machine learning algorithms have been developed in these highly dynamic times to aid in resolving complex real-world problems. This post will teach you about ten well-known machine-learning algorithms and the many learning methods that may be utilized to turn these algorithms into practical machine-learning models.
- Linear Regression
Linear regression is a potent statistical technique for predicting continuous data. It simulates how a scalar response and one or more explanatory factors interact. Simple linear regression is used when there is only one explanatory variable. However, multiple linear regression is used when several variables impact the response.
- Logistic Regression
Logistic regression is a classification approach for binary and multiclass classification issues. The logistic model in statistics is a statistical model that uses the event’s log odds as a linear combination of one or more independent variables to represent the likelihood that an event will occur. The parameters of a logistic model are estimated using logistic regression in regression analysis.
- Decision Trees
A decision tree-based model uses a tree structure to represent options and their likely outcomes. It is an effective tool for both classification and regression problems. This decision support method uses a tree-like structure to represent different options and their potential outcomes, considering utility, resource costs, and the likelihood of chance events.
- Random Forest
It is an expansion of the decision tree that uses many decision trees to improve prediction accuracy and reduce overfitting. The random forests or random decision forests ensemble learning strategy, which is used for classification, regression, and other tasks, builds a lot of decision trees during the training phase.
- K-Nearest Neighbours (KNN):
The prediction made by this non-parametric classification and regression approach is based on the average of the K data points that are closest to it. Evelyn Fix and Joseph Hodges first developed the k-nearest neighbors strategy in 1951, and Thomas Cover later enhanced it. It is a non-parametric supervised learning technique in statistics. It can be used for classification and regression.
- Support Vector Machines (SVM)
It is a potent method for tackling problems, including outlier detection, classification, and regression. Hyperplanes are used to group data points into categories. Support vector machines (SVMs) are supervised learning models with associated learning algorithms used in machine learning to analyze data for regression and classification.
- Naive Bayes
The probabilistic naive Bayes classifier performs well in high-dimensional situations with sparse inputs. It is a member of the family of Bayesian classifiers that strongly assume feature independence and are based on the theorem. Naive Bayes can achieve excellent accuracy in Bayesian network models despite being straightforward when paired with kernel density estimation.
- Gradient boosting and AdaBoost
Gradient boosting is a flexible machine-learning method for regression and classification tasks. It uses a group of decision trees, often weak learners, to generate a prediction model. Yoav Freund and Robert Schapire unveiled the statistical classification meta-algorithm AdaBoost in 1995. AdaBoost’s effectiveness can be increased even more by combining it with different learning techniques, increasing its prediction capacity.
- Convolutional Neural Networks (CNNs)
This deep learning method is often used in speech recognition, picture classification, and computer vision problems. In deep learning, convolutional neural networks are a particular kind of artificial neural network widely used to analyze visual input.
- Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) are a subtype of deep learning algorithms that are particularly adept at handling sequential data, such as time series, speech recognition, and natural language processing. In a recurrent neural network, which is a member of the family of artificial neural networks in which connections between nodes can create a cycle, the output from some nodes might affect subsequent input to the same nodes.
