Training data for Machine Learning (ML) is a key input to algorithm that comprehend from such data and memorize the information for future prediction. Although, various aspects come during the ML development, without which various crucial tasks cannot be accomplished.
Amid, training data is a backbone of entire AI and ML project without that it is not possible to train a machine that learns from humans and predict for humans. Hence, we right here will discuss about the importance of training data in machine learning with various set of examples to encourage AI or ML engineers make sure they have right and accurate data sets while working on such projects to get right result.
An Organized form of Unorganized Data
Data collected from multiple sources are usually available in unorganized format, which is not useful for machines to ingest the useful information. But when such data is labeled or tagged with annotation it becomes a well-organized data that can be used to train the AI or ML model.
And annotated or labeled data helps machines through computer vision to detect various objects from the group and store the information for future reference. Training data not necessary means, you should have labeled or annotated data sets, instead an organized data sets is also very important for machine learning model training.
Recognition and Classification of Objects
Another most important role of training data for machine learning is classifying the data sets into various categorized which is very much important for supervised machine learning. For an example, if you want your algorithm to recognize these two different species of animals – say a cat and dog, you need labeled images containing these two class of animals.
When your algorithm learns what are the features are important in distinguishing between two classes. It helps them to recognize and classify the similar objects in future, thus training data is very important for such classification. And if it is not accurate it will badly affect the model results, that can become the major reason behind the failure of AI project.
Provides a Key Input to ML Algorithms
To work with ML algorithm you need certain inputs making your model understand the things in its own way. And training data is the only source, you can use as an input into your algorithms, that will help your AI model to gain the useful information from the data and take crucial decisions like human intelligence do.
In a supervised machine learning, an additional input of labeled training data is required. And when your training data is not properly labeled, its not worth for supervised machine learning. The data like images are annotated with precise metadata making the object recognizable to machines through computer vision. Hence, training data as a key input need to be accurate in terms of labeling with right procedure.
Validating the Machine Learning Model
Merely developing an AI model is not enough, you need to validate the model to check its accuracy, so that you can ensure the prediction quality in real-life. To validate or evaluate such AI model you need another set of training data which can be also called the validation data, use to check the accuracy level of model in different scenario.
During ML model validation again labeled data is used to cross-check, whether machines has correctly detected the object or not. Training data is already labeled and if machine is unable to recognize the object, means either your labeled data is not right or algorithm is not capable to train your model in recognizing such things precisely. Once, you have checked the output given by machine you have to validate if its correct or not.
Understanding the importance of training set in machine learning will help you to gather the right quality and quantity of training data for your model training. Once you realize how its important and how it affects the model prediction, you will also choose the suitable algorithm as per your training data set availability and compatibility.
Hence, while working with AI and ML model, giving the priority to training data will definitely help you to acquire the best quality of data sets to get best results. Cogito is one of the companies, providing the machine learning training data with labeling and annotation for AI model development into various fields, while ensuring the quality and accuracy at best level.