List of Popular Machine Learning Algorithm
Here is a list of the Top 10 Most popular Machine Learning Algorithms.
1. Linear Regression
Linear regression is a simple algorithm used to map the linear relationship between input features and a continuous target variable. It works by fitting a line to the data and then using the line to predict new values.
2. Logistic Regression
Logistic regression is an extension of linear regression that is used for classification tasks to estimate the likelihood that an instance belongs to a specific class.
3. SVM (Support Vector Machine)
SVMs are supervised learning algorithms that can perform classification and regression tasks. It finds a hyperplane that best separates classes in feature space.
4. KNN (K-nearest Neighbour)
KNN is a non-parametric technique that can be used for classification as well as regression. It works by identifying the k most similar data points to a new data point and then predicting the label of the new data point using the labels of those data points.
5. Decision Tree
Decision trees are a type of supervised learning technique that can be used for classification as well as regression. It operates by segmenting the data into smaller and smaller groups until each group can be classified or predicted with high degree of accuracy.
6. Random Forest
Random forests are a type of ensemble learning method that employs a set of decision trees to make predictions by aggregating predictions from individual trees. It improves the precision and resilience of single decision trees. It can be used for both classification and regression tasks.
7. Naive Bayes
Naive Bayes is a probabilistic classifier based on Bayes’ theorem that is used for classification tasks. It works by assuming that the features of a data point are independent of each other.
8. PCA (Principal Component Analysis)
PCA is a dimensionality reduction technique used to transform data into a lower-dimensional space while retaining as much variance as possible. It works by finding the directions in the data that contain the most variation, and then projecting the data onto those directions.
9. Apriori algorithms
Apriori algorithm is a traditional data mining technique for association rules mining in transactional databases or datasets. It is designed to uncover links and patterns between things that regularly co-occur in transactions. Apriori detects frequent itemsets, which are groups of items that appear together in transactions with a given minimum support level.
10. K-Means Clustering
K-Means clustering is an unsupervised learning approach that can be used to group together data points. It works by finding k clusters in the data so that the data points in each cluster are as similar to each other as feasible while remaining as distinct as possible from the data points in other clusters.
Discover the fundamental concepts driving machine learning by learning the top 10 algorithms, such as linear regression, decision trees, and neural networks.
Machine Learning Algorithms
Machine learning algorithms are computational models that allow computers to understand patterns and forecast or make judgments based on data without the need for explicit programming. These algorithms form the foundation of modern artificial intelligence and are used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, fraud detection, autonomous cars etc.
This Machine learning Algorithms article will cover all the essential algorithms of machine learning like Support vector machine, decision-making, logistics regression, naive bayees classifier, random forest, k-mean clustering, reinforcement learning, vector, hierarchical clustering, xgboost, adaboost, logistics, etc.