Transfer learning is a technique where a pre-trained model is used as a starting point for a new machine learning model. This approach can be useful when the amount of available training data is limited, or when the task at hand is similar to the task that the pre-trained model was originally trained on. In …
Data is the foundation of all machine learning algorithms. In order for a machine learning model to learn and make accurate predictions, it needs to be trained on large amounts of relevant and diverse data. The quality and quantity of data used to train a model can greatly impact its performance and accuracy. It’s important …
Overfitting occurs when a machine learning model becomes too complex and starts to fit the training data too closely, resulting in poor generalization performance on new, unseen data. This happens because the model has essentially memorized the training data instead of learning the underlying patterns and relationships. One common cause of overfitting is having too …
Today, let’s talk about the differences between supervised and unsupervised learning in machine learning. Supervised learning is a type of machine learning where the model is trained using labeled data, meaning that the training data includes input features as well as corresponding output labels. The goal of supervised learning is to learn a mapping between …
Today, let’s talk about bias in machine learning. Bias in machine learning refers to the phenomenon where a model learns and perpetuates stereotypes or prejudices present in the training data. This can lead to unfair or discriminatory outcomes, such as certain groups being disproportionately impacted by the decisions made by the model. One of the …
