Cognegica Networks provides a training data set for Supervised Learning Engines which includes segmentation, labelling and annotation of speech, audio, video, text and image for machines to  learn and improves itself through data analysis and algorithms.

What is a Training Data Set

It is a world of data today! Machine learning (ML) engines need training data to understand the expected outcomes and behavior. This data is injected into the machines to train them. Thus, training data needs to fulfill the criteria of being relevant, accurate, and adequate. A low-quality training data can be the worst possible news for your machine learning program. The training data set lays a solid foundation on which the machine learning engine and its algorithms learn their baby steps in the world of Artificial Intelligence (AI).

At Cognegica Networks, we provide quality NLP (Natural Language Processing) and AI training data sets to our clients, based upon their custom machine learning models and algorithms.

The Types of Training Data Sets

Today, the artificial intelligence world is a mammoth one; it comprises many applications from robotics, healthcare, automotive to home appliances, and much more. So, the kind of training given to the AI machines when they get commissioned for these numerous applications is also understandably varied and unique.

It leads to the variety in the training data sets used to train the ML engines as per the goal. These are mainly of two types:

1. Labeled or Annotated training dataset

Data sets are labeled or annotated based on imperative considerations for the training of machines. The point of labeling in the data is crucial as it conveys vital information to the algorithm that facilitates getting the desired outcomes from it.

For example, in a video data set, one can annotate for object detection. In an NLP training data set, one can annotate the intent, sentiment, speaking style, accent, and much more. You can train your AI models to recognize speech, user intent, etc., with an NLP training data set.

2. Unlabelled or Unannotated training dataset

These data sets are not labeled by ‘Humans in the loop’! Humans in the loop are the people who collect the requisite training data and ready it for the algorithms. Instead, ML and AI models use such data sets to infer patterns and information. The algorithm determines these patterns usually from the knowledge gathered from a pre-entered labeled training data set.

Cognegica Networks also provides training data for Language Localization services. Our team of linguistic experts makes sure that clients benefit in various locations throughout the world, assimilating the language and cultural nuances of that place. 

How much training data is sufficient data?

As with all training, the more superior data quality you possess, the better the coaching. Training data sets more often than not mimic real-life conditions; they are not a static set.

We at Cognegica Networks provide NLP training data sets. We use a large volume of audio and textual data to convey mood, intent, accent, pronunciation, and so much more as feeding material. This data trains the machine learning and artificial intelligence algorithms to identify and evaluate various kinds of similar data but in a real-world scenario that ranges from simple to complex. The more varied and accurately annotated is the data, the more chances that the machine performs smoothly under all circumstances.

How well the machine performs under different use cases is a great way to test the quality and the quantity of training data. A test data set is also prepared and is run to check the various possible scenarios after the models get trained with the training data. You can thus evaluate the understanding of the algorithms and the expected outcomes. The better the machine performs, the better is the understanding and efficiency of your algorithm.

Training procedure

Following are the steps employed in the use of training data sets:

1. Gather the raw data for the specific domain.
2. Enrich the data by carefully labeling it and transcribing it as per what you want the machine to learn and get trained.
3. Feed these training data sets into the AI model or ML engine.
4. Run the scenarios and evaluate algorithmic output accuracy based on pre-determined calculations using a separate validation or test data set.
5. If the algorithm performs short of your expectations, repeat the above steps in order.
6. If the output matches the expected outcome, it means the algorithm has received sufficient information and is well-trained.

You can rest assured that the training data provided by Cognegica Networks to train your artificial intelligence and machine learning engines will be of high caliber. You can contact us here for this service.