SVMs are consistently the go-to method for a high-performing algorithm with little tuning. This means less work is required in the creation of a reliable AI model. SVMs are supervised learning ...

Supervised learning algorithms are trained on input data annotated ... a good indication of performance — it might also mean the model is suffering from overfitting, where it’s overtuned ...

Decision trees are a supervised learning model that can be used for either regression or ... a technique that involves training the same algorithm with different subset samples of the training data.

We have previously discussed several supervised learning algorithms, including logistic ... Neither SVM nor kNN make explicit model specifications about the data-generating process such as ...

Supervised and unsupervised learning describe two ways in which machines - algorithms - can be set ... But once you’ve done that, in theory it will continue to teach itself as it reads more ...

A regression problem is a supervised learning problem that asks the model to predict a number. The simplest and fastest algorithm is linear (least squares) regression, but you shouldn’t stop ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic ...

Choose the correct learning model. There are different types of learning approaches you can choose when building an ML algorithm such as supervised learning, unsupervised learning, semi-supervised ...

In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning ...