The greatest headache for any machine learning engineer is the problem of overfitting. The model we trained works perfectly on the training dataset but when…
Gradient descent is an optimization method used to find the minimum value of a function by iteratively updating the parameters of the function. Parameters refer…
Principal component analysis is a method used to reduce the number of dimensions in a dataset without losing much information. It’s used in many fields…
A Classification report is used to measure the quality of predictions from a classification algorithm. How many predictions are True and how many are False.…
Read my previous post to understand how K-Means algorithm works. In this post I will try to run the K-Means on Iris dataset to classify…
K-Means is one of the simplest unsupervised clustering algorithm which is used to cluster our data into K number of clusters. The algorithm iteratively assigns…
Support Vector machines (SVM) can be used for both classification as well as regression tasks but they are mostly used in classification applications. Some of…
Previously I wrote an article explaining the underlying maths behind polynomial regression. In this post I will use Python libraries to regress a simple dataset…
Polynomial regression is a process of finding a polynomial function that takes the form f( x ) = c0 + c1 x + c2 x2 ⋯ cn xn where n is the degree of the polynomial and c is a set of coefficients. Through…
Previously we built a simple linear regression model using a single explanatory variable to predict the price of pizza from its diameter. But in the…