Supervised learning algorithms learn from labeled data, where the desired output is known. These algorithms aim to build a model that can predict the output for new, unseen input data. Let’s take a ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Imagine you decide to sell your home. You contact a realtor and ask them to value your house. However, if the realtor doesn’t offer any insight into how they arrive at their estimate—for example, what ...

AI (Artificial Intelligence) is a broad concept and its goal is to create intelligent systems whereas Machine Learning is a specific approach to reach the same goal.

This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

The Nobel Prize in Physics was awarded to two scientists for discoveries that laid the groundwork for the artificial intelligence. British-Canadian Geoffrey Hinton, known as a 'godfather of AI', and ...

Learn how recommendation algorithms, streaming recommendations, and social media algorithms use content recommendation ...

Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...

Overview: AI in financial services uses machine learning and automation to analyze data in real time, improving speed, accuracy, and decision-making across bank ...