![[Algorithms] - Multi class classification](/content/images/2021/10/multiclass-clasiffers.png)
From Techopedia: Linear multiclass classification is a specific kind of targeted algorithm philosophy in machine learning and the field of structured prediction that uses both linear and multiclass methods. A multiclass classification is
![[Algorithms] - Support Vector Machines](/content/images/2021/10/svm.png)
A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problem. Compared to newer algorithms like neural networks, they have two main advantages: higher speed
![[Algorithms] - Logistic Regression](/content/images/2021/09/image-6-1.png)
Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data
Ridge Regression Ridge regression learns , using the same least-squares criterion but adds a penalty for large variations in parameters. The addition of a penalty parameter is called regularization. Regularization is an important concept
Before writing the next post about Algorithms, I thought it was important to talk first about feature normalization, as it will be relevant in almost all algorithms moving forward. Some of the algorithms
![[Algorithms] - Linear regression Least- Squares](/content/images/2021/09/2021-09-17-15_45_44-LinearRegressionLeastSquares---Jupyter-Notebook-1.png)
A linear model is a sum of weighted variables that predict a target output value given an input data instance. For example: car prices. A car has different features like: year built, horse