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...
The Importance of Data Quality for Machine Learning: How Bad Data Kills Projects
Despite fears of AI taking over the world, at its core, AI is still reactionary and doesn’t truly learn on its own. It can only make predictions based on the data it’s given. That is why data quality for machine...
What Is Named Entity Recognition in NLP?
Named entity recognition (NER) is a vital subfield of natural language processing (NLP). In short, NER aims to identify and extract named entities, such as people, organizations, locations, and other specific details, from unstructured text data. NER is widely used...
Examining the Elements of Spatial Data Science
Spatial data is so ever-present that we tend to forget about its significance – from the simplest of maps to weather forecasts to GPS – all of this is based on spatial data. Spatial data science simply takes this to...
The Challenges of Text, Audio, Photo, and Video Data Collection for ML Training Models
The basis of all machine learning projects is data collection and acquisition. But that is also the first stumbling block where many ML projects fail. In this article, we will discuss the challenges of text, audio, photo, and video data...
Geospatial AI Solutions and Use Cases Explained
While many consider the technology to still be in its infancy, geospatial AI solutions have already demonstrated the ability to automate many manual processes and provide analyses far more quickly and more accurately than humans can, while using fewer resources...
What Is Geospatial Data Analysis? – and How It Is Leveraged by Businesses
In an era where data drives decision-making across industries, geospatial data analysis has emerged as a critical tool for understanding our world in a whole new dimension. From urban planning to disaster management, geospatial data allows for deeper insights into...