While many believe that big data is essential for training machine learning algorithms, it's not always the case. There are various methods, such as using lightweight algorithms, fine-tuning pre-trained models, and employing active learning, that allow for effective training with smaller datasets.

Overfitting occurs when a machine learning algorithm learns the training dataset too well, including its noise, and fails to generalize to new, unseen data. This can lead to false dependencies, where the model only recognizes specific patterns, such as only identifying black cats if that was the only data it was trained on.

To reduce the amount of data needed, you can use techniques like feature engineering to create relevant indicators, fine-tune pre-trained models that require less data, and implement active learning, where the algorithm identifies which examples need labeling. These approaches can help in achieving effective training without relying solely on large datasets.