Learning Patterns with Training Datasets

In this lesson, we will explore Training Datasets, which are used by machine learning models during their learning process.

A training dataset is data utilized by models to learn patterns that solve a specific problem.

Using this data, models learn to find patterns and perform predictions.

When a model learns the relationship between inputs and correct answers (labels) through the training dataset, it gains the ability to predict new data.

For instance, imagine training a machine learning model to classify dogs and cats.

In this case, the training dataset is structured as follows:

• Input values: Images of various breeds of dogs and cats

• Correct answers (labels): Information indicating whether each image is of a dog or a cat

The model learns the patterns to differentiate between dogs and cats through numerous images, enabling it to classify new images as either a dog or a cat.

The quality of the training dataset is crucial for the model to learn effectively.

To compose a good training dataset, the following conditions should be met.

The more data available, the more patterns the model can learn.

For example, to create an AI model distinguishing between dogs and cats, you typically need at least 5,000-10,000 images per class.

It should include diverse samples instead of being biased towards a specific type of data.

For instance, when training the cat class, the training dataset should consist of images taken from various breeds, backgrounds, and angles.

Ensure the dataset does not contain incorrect labels, and enhance data quality through preprocessing.

For example, it's necessary to assign correct labels to unlabeled dog/cat images or correct any erroneous labels.

Evaluating a machine learning model using only the training dataset might lead to overestimating the model's performance.

Therefore, it is vital to use validation datasets and test datasets separately from the training dataset.

In the next lesson, we'll take a detailed look into validation datasets.