Top 10 Machine Learning Algorithms to Learn*

1. Linear Regression: Predicts continuous values based on a linear relationship.
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2. Logistic Regression: Predicts binary values based on a logistic relationship.
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3. Naive Bayes: Uses a combination of independent features such as size, color, shape to predict the probability of an object belonging to a particular class (e.g. a plant).
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4. Decision Tree: Classifies data by creating a tree of decisions. Operates like a flowchart, starting with a root node that poses questions, guiding the data down branches with subsequent nodes until it reaches an end, known as a leaf node.
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5. Random Forest: Instead of relying on a single decision tree, a random forest combines the predictions from multiple decision trees to make more accurate predictions.
How to use Random Forest
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6. KNN: K-Nearest Neighbors: classifies an output based on its proximity to other data points on a graph.
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7. K-means: Groups data together based on similarity.
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8. SVM: Support Vector Machine: Classifies data by creating a "hyperplane." In 2D, which is like a line that separates two sets of labeled data.
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9. Apriori: analyzes transactional data to identify frequent item sets, which are combinations of items often occurring together, e.g., predicting that buying product A increases product B's likelihood.
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10. Gradient Boosting: creates a series of "weak" models that iteratively improve to form a robust predictive model, starting with a basic model and refining subsequent models in each iteration to correct past errors.
Beginner Guide
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Related Video: Top 5 Machine Learning Books for Beginners

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