Top 10 Machine Learning Algorithms for Beginners

Machine Learning is a subset of artificial Intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In ML, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.

If you are a beginner, these are the top 10 algorithms you should start with.

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1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.

2. Logistic Regression: Used for binary classification problems, where the output is a binary value( 0 or 1)

3. Decision Trees: can be used for both classification and regression tasks, based on a tree-like structure of decisions.

4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.

5. Support Vector Machines: Used for both classification and regression tasks, to find the hyperplane that best supports the classes.

6. K Nearest Neighbours(KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbours in the feature space.

7. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.

8. Naive Bayes: A probabilistic algorithm based on Bayes’ theorem that is commonly used for text classification and spam filtering.

9. Principal Component Analysis(PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

10. Gradient boosting Machines: An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.