10 Must-Know Machine Learning Algorithms for Beginners
1️⃣ Linear Regression:
Predicts a continuous outcome by fitting a linear relationship between independent and dependent variables.
2️⃣ Logistic Regression:
Estimates binary outcomes by predicting the probability of an event using a logit function.
3️⃣ Decision Tree:
Splits data into homogeneous sets based on significant attributes to classify or predict outcomes.
4️⃣ SVM (Support Vector Machine):
Classifies data by finding the optimal hyperplane that separates data points in an n-dimensional space.
5️⃣ Naive Bayes:
Predicts outcomes by assuming independence between features and calculating probabilities based on Bayes' theorem.
6️⃣ KNN (K-Nearest Neighbors):
Classifies data points based on the majority vote of their nearest neighbors in the feature space.
7️⃣ K-Means:
Groups data into K clusters by minimizing the distance between data points and cluster centroids.
8️⃣ Random Forest:
Combines multiple decision trees to improve prediction accuracy through majority voting.
9️⃣ Dimensionality Reduction Algorithms:
Reduces the number of features in data while preserving important patterns and relationships.
🔟 Gradient Boosting & AdaBoosting: Combines weak predictive models to create a strong model, improving accuracy and robustness.
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