Machine learning
Machine Learning is a term that has been used in Mathematics, Statistics and Computer science for quite a few decades. It traces itself back almost for a century in Mathematics. The primary objective of Machine Learning is to make an inference about an even based on certain input values. Students of statistics could immediately relate to probabilistic determination of outcomes of events based on certain input parameters while students of Mathematics could identify this with linear algebra and matrix transformations.
We are now living in an age dominated by cloud computing. You can easily find everything available with a click on a button on the cloud, be it the data or the processing of the data. Various platforms are readily available for the devices of IoT if your organization is looking for more powerful options…
Classification Naive Bayes Naive Bayes is a classification algorithm which checks the probability of a test point belonging to a class. Unlike other algorithms, Naive Bayes is purely a classification algorithm. As explained in the last release this is the formula for the Bayes theorem and as the algorithm name suggests Naive Bayes is inspired…
Machine learning algorithms can broadly be classified into 3 broad types. 1. Supervised Learning: In this type of algorithm, there is a target variable that we wish to predict. This target variable is dependent on various independent variables. A function is generated using this set of variables that gives the desired outputs. The model is…
In layman’s terms, Ridge regression adds one more term to linear regression’s cost function to reduce error. Ridge regression is a model-tuning method used to analyze data that suffer from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large; this results in predicted values being…
In supervised learning, overfitting is a major problem. When it comes to regression trees, it becomes more obvious to prevent this, we specify the Seth of the tree, but there is still a high chance of the dataset being overfitted. To avoid this, we use a random first regressor. Before understanding how and why this…
The decision tree is one of the most commonly used algorithms in supervised learning. You can use it for both regression and classification when we use this algorithm for regression problems, it is called a regression tree. In real life, decision trees are very famous algorithms they are used to predict high occupancy dates for…
“Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth-degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y.” As stated in formal terms, this definition of polynomial regression…
There are situations in daily life where we want to know the relationship between various factors, for example, if the price of petrol increases would it affect the sales of cars or does changing the location of the house will it affect the price. The process of finding this relationship between multitudes of factors is…
K Nearest neighbour (KNN) is one of the most basic classification algorithms. It follows the assumption that similar things exist in close proximity to each other. The K-nearest neighbour algorithm calculates the distance between the various points whose category is known and then selects the shortest distance for the new data point. It is an…
Naive Bayes is a classification algorithm which checks the probability of a test point belonging to a class. Unlike other algorithms, Naive Bayes is purely a classification algorithm. As explained in the last release this is the formula for the Bayes theorem and as the algorithm name suggests Naive Bayes is inspired by the Bayes…
