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.