Category Archives: Data Science News

Artificial Intelligence (AI) is getting integrated into vital fields making human life more efficient and productive. Similarly, AI in agriculture is making agriculture and farming easier with computer vision-based crop monitoring and production system. AI Robots, drones and automated machines are playing a big role in harvesting, ripping, health monitoring and improving the productivity ofContinue reading “Role of Image Annotation in Applying Machine Learning for Precision Agriculture”

3D cuboid image annotation technique is one of the critical process, helps machines to recognize the all the three dimensions of the objects. This image annotation process is used to detect the objects in-depth that helps to train the 3D visual perception model. Actually, this image annotation technique helps to build the ground truth datasetsContinue reading “How 3D Cuboid Annotation Service is better than free Tool?”

This is life in the 21st century. Your remote and your tv are both your smartphone now. ? The smartphone is the portal gun therefore… Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/creative-destruction-and-godlike-technology-in-the-21st-century-a8cd3069c468?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/creative-destruction-and-godlike-technology-in-the-21st-century

The first step of any data science project is data collection. While it can be the most tedious and time-consuming step during your workflow, there will be no project without that data. If you are scraping information from the web, then several great tools exist that can save you a lot of time, money, andContinue reading “6 Web Scraping Tools That Make Collecting Data A Breeze”

Lessons from network science and the difficulty of graph anonymization. A data scientist’s take on the difficultly of striking a balance between privacy and utility in anonymizing connected data. Originally from KDnuggets https://ift.tt/3pNIPSc source https://365datascience.weebly.com/the-best-data-science-blog-2020/the-difficulty-of-graph-anonymisation

By reading papers, we were able to learn what others (e.g., LinkedIn) have found to work (and not work). We can then adapt their approach and not have to reinvent the rocket. This helps us deliver a working solution with lesser time and effort. Originally from KDnuggets https://ift.tt/3aRIv0G source https://365datascience.weebly.com/the-best-data-science-blog-2020/how-reading-papers-helps-you-be-a-more-effective-data-scientist

Supply chain management is a complex medley of processes in which even a slight lack of visibility or synchronization can lead to enormous losses and overheads. But with the recent developments in AI & machine learning, we can now harness historic and real-time supply chain data to discover patterns that help us understand what factorsContinue reading “How Is Machine Learning Revolutionizing Supply Chain Management”

New Oil that Business Needs to Run Data has been compared to a well-known phrase: new oil that business needs to run. IBM CEO Ginni Rometty explains it on the World Economic Forum in Davos in 2019, “I think the real point to that metaphor,” Rometty said, “is value goes to those that actually refine it,Continue reading “How to Make Data Annotation More Efficient?”

The purpose of this tutorial is to demonstrate the power of algorithmic labelling through a real world example that we had to solve ourselves.* If you want to see the resulting full labelled dataset from this process, sign up here. In a later post we will go over a more thorough description of what algorithmic labellingContinue reading “Label a Dataset with a Few Lines of Code”

At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead — early in the planning — toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system. Originally from KDnuggets https://ift.tt/2P9jHszContinue reading “Why Do Machine Learning Projects Fail?”