Acquiring Labeled Data to Train Your Models at Low Costs
An untrained statistical model is like a Ferrari that simply will not run. In other words, it is simply not of much use. Supervised Learning is based on the availability of high quality labeled data. Labeled data is the ingredient that will make your Ferrari ( statistical model) roar. To put it in technical terms, labeling your training data gives your model the ability to correctly predict, classify and otherwise analyze data to generate meaningful output.
Rule of thumb, it’s best not to develop a model if you haven’t figured out how to first acquire and then, more importantly, label(‘tag’ or ‘annotate’) a suitable training data set.
Labeling is a tedious and time-consuming affair, isn’t it? Check out these ingenious methods that you can use to get your data set labeled without breaking the bank.
It is much easier to use pre-trained models to generate the insights you are looking for. These plug-and-play AI models save you the effort of creating a functional model, labeling and training your data set. All you need is raw data which can be plugged into a well-curated and reliable model which will generate useful metrics for you.
A whole host of such APIs has been developed by the ParallelDots team. Using these models is very convenient and result-driven. Some of the standard functionalities addressed within text analysis are – sentiment analysis, emotion detection, keyword extractor, semantic similarity and much more. Within the visual intelligence space, ParallelDots has created an object recognizer and facial emotion detection.
These Machine learning algorithms can learn from unlabeled input data.Unsupervised learningalgorithms are a step towards automated machine learning. This approach removes the problem of labeling right from the root. Instead of responding to feedback, unsupervised learning identifies common points in the data and reacts based on the presence or absence of such commonalities in each new piece of data.
