Must-Know Algorithms used in Machine Learning Applications

Predicting the future by fitting a line to data points. It's the foundation of predictive modeling.

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A forest of decision trees that work together to make more accurate predictions. Versatile and robust.

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Grouping data points into clusters based on similarity. Essential for segmentation and pattern recognition.

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Probability-based classification that's fast and efficient. Widely used in spam filters and sentiment analysis.

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Reducing dimensionality while retaining critical information. Crucial for data preprocessing.

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Must-Know Algorithms used in Machine Learning Applications

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Predicting the future by fitting a line to data points. It's the foundation of predictive modeling.

1

Linear Regression

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Decision Trees

Making decisions step by step, branching out to find the best outcome. They're used for classification and regression tasks.

A forest of decision trees that work together to make more accurate predictions. Versatile and robust.

3

Random Forest

Grouping data points into clusters based on similarity. Essential for segmentation and pattern recognition.

4

K-Means Clustering

5

Support Vector Machines

Drawing the line between data points, ideal for classification tasks with clear boundaries.

Probability-based classification that's fast and efficient. Widely used in spam filters and sentiment analysis.

6

Naive Bayes Classifier

7

Neural Networks

Mimicking the human brain to solve complex problems. Deep Learning has revolutionized AI with these.

8

Gradient Boosting

Combining weak models into a strong one, boosting predictive power in various domains.

Reducing dimensionality while retaining critical information. Crucial for data preprocessing.

9

Principal Component Analysis (PCA)

10

Reinforcement Learning

Teaching machines to learn from experience and make optimal decisions in dynamic environments.

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