Let's explore the basic Machine Learning algorithms
If you are a machine learning engineer or data scientist, you can use these techniques to create functional Machine Learning projects easily. These four basic machine learning algorithms are the base of artificial intelligence and Machine Learning.
Here is a detailed video about the Top 10 Machine Learning Algorithms For Data Scientists
1. Gradient Descent
Gradient Descent is an optimization algorithm. Optimizers help to converge a machine learning model during training by reducing the loss. Loss is nothing but the difference between the actual value and predicted value.
A model converges means, there is very little possibility of a further significant decrease in loss, if we do more training iteration. In other words, a model converged, means there is very little possibility of a significant increase in performance, by doing more training iteration.
You can see the implementation in Gradient Descent from Scratch in Python.
2. Linear Regression
Linear regression is one of the most fundamental and basic, machine learning algorithms. It is used to show, the relationship between, two or more variables.
The column values, that are used to predict the output, are called features, or independent variables. And the column value, that is predicted, is called the dependent variable, or label.
You can see the implementation in Linear Regression from Scratch in Python.
3. Logistic Regression
Logistic regression is one of the most fundamental and basic, machine learning algorithms. Logistic regression is a machine learning algorithm, used for classification problems.
It uses, the log odds ratio, and maximum likelihood method, to fit the model.
The column values, that are used to predict the output, are called features, or independent variables. And the column value, that is predicted, is called the dependent variable, or label.
You can see the implementation in Logistic regression from Scratch in Python.
4. Decision Tree
The decision tree algorithm, is a machine learning technique, for making predictions. As its name suggests, it behaves like a tree structure. The decision tree is built by, repeatedly splitting, training data, into smaller and smaller samples.
It works on, the principle of conditions. We check conditions, at a node, and split the data, as per the result, of the conditional statement.
Decision Tree algorithm belongs to, the family of, supervised machine learning algorithms. It can be used to, build classification, as well as regression models.
You can see the implementation in the Decision tree from Scratch in Python.
End Notes:
In this article, we discussed the Top 4 commonly used Machine Learning Algorithms and I hope you found this article useful.
You may like to explore more Machine Learning algorithms in the below video.
Happy Learning !!
