Machine learning engineers are responsible for developing and deploying machine learning models. In order to do this effectively, they need to have a strong understanding of machine learning algorithms.
Here are the top 10 algorithms that every machine learning engineer should know:
Linear regression: Linear regression is a simple but powerful algorithm that can be used to predict continuous values.
Logistic regression: Logistic regression is a type of regression that can be used to predict categorical values.
Decision trees: Decision trees are a popular algorithm for classification and regression tasks.
Support vector machines: Support vector machines are a powerful algorithm for classification and regression tasks.
Random forests: Random forests are an ensemble algorithm that combines multiple decision trees.
K-nearest neighbors: K-nearest neighbors is a simple but effective algorithm for classification and regression tasks.
Naive Bayes: Naive Bayes is a simple but effective algorithm for classification tasks.
Principal component analysis: Principal component analysis is a dimensionality reduction algorithm that can be used to reduce the number of features in a dataset.
Singular value decomposition: Singular value decomposition is another dimensionality reduction algorithm that can be used to reduce the number of features in a dataset.
Neural networks: Neural networks are a powerful algorithm for machine learning tasks that require learning complex relationships between features.
These are just a few of the most important machine learning algorithms. There are many other algorithms that machine learning engineers should be familiar with. However, these 10 algorithms are a good starting point for any machine learning engineer.
