Artificial Intelligence (AI) and machine learning training dataset are becoming the new tool for developers to create a more efficient and life-changing models brining an intelligence into machines to perform various tasks into business operations and household without help of humans.
And to develop the AI and ML model, a precise training data is required that help algorithms to understand the certain patterns or series of outcomes comes to a given question. And training data can consist texts, images or videos which are mainly labeled to make it recognizable to computer vision and understandable to machines.
What is Training Data?
Training data is basically a type of data used for training a new application, model or system through various methods depending on the project’s feasibility and requirements. And training data for AI or ML is slightly different, as they are labeled or annotated with certain techniques to make it recognizable to computer that helps machines to understand the objects.
The majority of training data contains the pair of input gathered from the various resources and then organized and annotated with certain techniques with accuracy. The data could be different due to model algorithms and the field for which it is developed while ensuring the accuracy level to make sure the prediction should be accurate.
Types of Training Data for Machine Learning
In machine learning training data is the key factor to make the machines recognize the objects or certain patterns and make the right prediction when used in real-life. Basically, there are three types of training data used in machine learning model development and each data has its own importance and role in building a ML model.
Training data is the main and most important data which helps machines to learn and make the predictions. This data set is used by machine learning engineer to develop your algorithm and more than 70% of your total data used in the project. A huge quantity of datasets are used to train the model at best level to get the best results.
This is the second type of data set used to validate the machine learning model before final delivery of project. ML model validation is important to ensure the accuracy of model prediction to develop a right application. Using this type of data helps to know whether model can correctly identify the new examples or not.
However, using the validation data also face the problem of over-fitting where AI has been wrongly trained to identify the examples that are too specific to the training data. And in such cases data scientists, often again use the training data and run through it again adjust values and hyper parameters to make the model more accurate.
This is the final and last type of data helps to check the prediction level of machine learning and AI model. Its is similar to validation data in testing the model accuracy but don’t help to improve the prediction level. It is basically used to test the model whether it will work well in real-life use and final test in the moment of truth for the model, if it works perfectly.
Here it is notable that all these three types of data are important for right ML model development. All these data sets together helps to ensure that all the examples are consistent and relevant to the answer expected from the model. But data scientist and Machine Learning Training Data engineers should split and organize the data into three categories randomly to avoid selection.
Why Training Data is Important for AI and ML?
Its true, without training data AI or ML is not possible. The quality, relevancy and availability of your data directly affects the goals of AI model. Incomplete or inaccurate data sets will train your AI model like a illiterate human that can’t understand his environment better. Hence, choosing the right data for your model will also help you to get the accurate results. Hence, your AI deserves best data that are precisely annotated and labeled that can only help your AI model to achieve the best level of accuracy at affordable cost.