Artificial Intelligence

This post explains how machine learning can help with text analysis and language processing (NLP). You will learn about the most popular ML text analysis techniques and their practical applications.

Find out why ML optimization is necessary and how ML optimization techniques work through simple, practical examples.

In machine learning, testing is mainly used to validate raw data and check the ML model's performance. Learn more about it in our guide.

What is pattern recognition, when and where is it used in machine learning? And how to train a pattern recognition system? Find out the answers in this post.

The difference between deep learning and neural networks is often confusing for beginners. What does it mean for an algorithm to be “deep”? What types of neural networks exist out there? You’ll learn all that and more in our guide.

Data preprocessing is the process of converting raw data into a well-readable format to be used by a machine learning model. Learn more about it in our article.

Discover over 50 open datasets that you can use for your own machine learning research.

Today, we will see how popular classification algorithms can help us, for example, to pick out and sort wonderful, juicy tomatoes.

Computer vision, text-to-speech, anomaly detection – learn about the most common fields where machine learning is used in business.

Need to build an ML model but don’t know where to start? In this post, we will help you pick the correct machine learning algorithms for your particular use case.

In this post, we are going to have a look at 18 popular machine learning platforms, frameworks, and libraries.

Read about the most commonly used machine learning algorithms and how they are categorized.