Top Machine Learning Algorithms

These Algorithms are the foundations of what you call AI today.

1️⃣ Linear Regression

->Used for predicting continuous values.

->Models the relationship between dependent and independent variables by fitting a linear equation.

2️⃣ Logistic Regression

->Ideal for binary classification problems.

->Estimates the probability that an instance belongs to a particular class.

3️⃣ Decision Trees

->Splits data into subsets based on the value of input features.

->Easy to visualize and interpret but can be prone to overfitting.

4️⃣ Random Forest

->An ensemble method using multiple decision trees.

->Reduces overfitting and improves accuracy by averaging multiple trees.

5️⃣ Support Vector Machines (SVM)

->Finds the hyperplane that best separates different classes.

->Effective in high-dimensional spaces and for classification tasks.

6️⃣ k-Nearest Neighbors (k-NN)

->Classifies data based on the majority class among the k-nearest neighbors.

->Simple and intuitive but can be computationally intensive.

7️⃣ K-Means Clustering

->Partitions data into k clusters based on feature similarity.

->Useful for market segmentation, image compression, and more.

8️⃣ Naive Bayes

->Based on Bayes' theorem with an assumption of independence among predictors.

->Particularly useful for text classification and spam filtering.

9️⃣ Neural Networks

->Mimic the human brain to identify patterns in data.

->Power deep learning applications, from image recognition to natural language processing.

🔟 Gradient Boosting Machines (GBM)

->Combines weak learners to create a strong predictive model.

->Used in various applications like ranking, classification, and regression.

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