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An algorithm in AI is a set of instructions for performing a task. Think of it as a recipe.

A learning algorithm is a special kind of algorithm used in machine learning to adapt and improve from data.

In short: All learning algorithms are algorithms, but not all algorithms are designed to learn. For example, sorting numbers is an algorithm task, while predicting future sales based on past data uses a learning algorithm.


Each machines learning algorithm shines in specific situations and may require different data structures and preprocessing techniques. 

To go more in detail on ML Algorithms:

  • Supervised Learning:
    • Linear Regression: Predicts continuous values.
    • Logistic Regression: Classification, typically binary.
    • Decision Trees: Decision-making tree structure.
    • K-Nearest Neighbors (KNN): Classifies based on majority vote of neighbors.
    • Artificial Neural Networks (ANN): Mimics brain neurons, good for complex tasks.
    • Naive Bayes: Classification based on Bayes’ theorem.
  • Unsupervised Learning:
    • K-Means Clustering: Divides data into ‘K’ number of clusters.
  • Ensemble Methods:
    • Random Forest: Collection of decision trees for robust predictions.
  • Recurrent Neural Networks (RNN):
    • Long Short-Term Memory (LSTM): A type of RNN, excels with sequences like time series.

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