Search Results for: "machine learning"

Coverfox

How Coverfox Utilises Machine Learning

Coverfox provides omni-channel and automated insurance experience to first-time insurance buyers, millenials and the rural population of India.

A Beginners’ Guide to Cross-Entropy in Machine Learning

Machine learning and deep learning models are normally used to solve regression and classification problems. In a supervised learning problem, during the training process, the model learns how to map the input to the realistic probability output.

Underrated But Interesting Machine Learning Concepts #2

In this series, we’ll look at several underrated yet fascinating machine learning concepts.

ML to predict terrorism

Can Terrorism Be Predicted With Machine Learning?

A team of researchers at Zhejiang University have developed a ML framework to predict and explain the occurence of terrorism.

A Series On Underrated But Interesting Machine Learning Concepts

Underrated but interesting machine learning concepts will be explored in this series.

How To Address Bias-Variance Tradeoff in Machine Learning

Bias and variance are inversely connected and It is nearly impossible practically to have an ML model with a low bias and a low variance. When we modify the ML algorithm to better fit a given data set, it will in turn lead to low bias but will increase the variance. This way, the model will fit with the data set while increasing the chances of inaccurate predictions. The same applies while creating a low variance model with a higher bias. Although it will reduce the risk of inaccurate predictions, the model will not properly match the data set. Hence it is a delicate balance between both biases and variance. But having a higher variance does not indicate a bad ML algorithm. Machine learning algorithms should be created accordingly so that they are able to handle some variance. Underfitting occurs when a model is unable to capture the underlying pattern of the data. Such models usually present with high bias and low variance. 

Understanding the AUC-ROC Curve in Machine Learning Classification

AUC-ROC is the valued metric used for evaluating the performance in classification models. The AUC-ROC metric clearly helps determine and tell us about the capability of a model in distinguishing the classes. The judging criteria being – Higher the AUC, better the model. AUC-ROC curves are frequently used to depict in a graphical way the connection and trade-off between sensitivity and specificity for every possible cut-off for a test being performed or a combination of tests being performed. The area under the ROC curve gives an idea about the benefit of using the test for the underlying question. AUC – ROC curves are also a performance measurement for the classification problems at various threshold settings. 

Beginner’s Guide to Online Machine Learning

On the other hand, online learning is a combination of different techniques of ML where data arrives in sequential order and the learner (algorithm/model) aims to learn and update the best predictor for future data at every step.

How to Prepare for AWS Certified Machine Learning Specialty?

AWS offers different certification programs to recognize talents in different fields. In the field of…

Why Data Scaling is important in Machine Learning & How to effectively do it

Scaling the target value is a good idea in regression modelling; scaling of the data makes it easy for a model to learn and understand the problem.

The Machine Learning Journey Of Aishwarya Srinivasan

I have never restricted myself to a specific domain or kind of modelling technique. I have always liked to explore new domains and learn new algorithms.

How Zomato Uses Machine Learning

How Zomato Uses Machine Learning

Zomato saw 3x improvement in its ‘feature store’ service compared to last year