What Is Audio Transcription and How Does It Relate to Data Labeling?
Audio transcription is the process of converting unstructured audio data, such as recordings of human speech, into structured data. Artificial intelligence (AI) and machine learning (ML) algorithms require structured data to perform various tasks involving human speech, including speech recognition,...
Semantic Segmentation for Computer Vision Projects Explained
Semantic segmentation is an image-labeling technique to create data for supervised computer vision (CV) models. In its simplest terms, the objective is to assign a class label to each pixel in an image. For example, if you’re labeling a cat...
Bounding Boxes in Computer Vision: Uses, Best Practices for Labeling, and More
Bounding boxes are rectangular region labels used for computer vision (CV) tasks. In supervised machine learning (ML), an object detection model uses bounding box labels to learn about the contents of an image. The bounding box labels objects or features of interest...
What Is Polygon Annotation and How Does it Work?
Polygon annotation is an essential labeling technique for supervised computer vision (CV). Objects are labeled with polygon annotations to create a dataset, which is fed into a supervised CV model. The model learns from the annotations, enabling it to predict...
What is Video Annotation for AI?
Computer vision (CV) was formerly focused on identifying and classifying information from still images but has now evolved to respond to complex video data. Video annotation has emerged as a critical component for developing AI applications that understand and respond...
The Most Important Natural Language Processing (NLP) Techniques Explained
Do you struggle to optimize your website’s keywords for search engine ranking? Are you tired of manually analyzing and selecting keywords for your content? Natural Language Processing (NLP) techniques may be your solution. NLP is a branch of artificial intelligence...
News, Research, and Social Media as Sources for Datasets
In the age of big data, datasets are crucial for research, analysis, and decision-making in various industries. But where do these datasets come from? Traditional sources, such as government agencies and academic institutions, are still important, but there is a...
The Benefits of Having a Human-in-the-Loop For Machine Learning and AI Projects
The power of machine learning and artificial intelligence goes beyond their flashy features and a futuristic vibe. Even though these technologies aren’t flawless, they have already transformed many industries and are poised to continue doing so in the future. From...
Basic Data Collection Methods for Machine Learning Projects Explained
Data collection might seem like a simple process on the surface. But because it’s the basic building block of all ML projects, the data needs to be accurate, relevant, and cover all iterations of a problem. Consequently, it is crucial...
A Comprehensive Guide to Data Acquisition for Machine Learning
You will hear many data scientists use the phrase ‘rubbish in, rubbish out’. In the world of machine learning, it means that an algorithm will not serve its intended purpose if the training data is no good. And professional data...
