But machine learning comes in many different flavors. In this post, we will explore supervised and unsupervised learning, the two main categories of machine learning algorithms.

This video is a one stop shop for understanding What is linear regression in machine learning. Linear regression in machine learning is considered as the basis or foundation in machine learning ...

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

The primary goal of a linear regression training algorithm is to compute coefficients that make the difference between reality and the model’s predictions consistently small.

Supervised learning in ML trains algorithms with labeled data, where each data point has predefined outputs, guiding the learning process.

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.

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 ...

Machine learning (ML) is a subset of artificial intelligence (AI) that allows computers to learn without being explicitly programmed.