Overfitting in machine learning refers to a scenario where a model learns the training data so well that it captures not only the underlying patterns and relationships but also the noise and random fluctuations present in the data. As a result, an overfitted model fits the training data too closely, resulting in a high degree of accuracy on the training set but a reduced ability to generalize to new, unseen data. Essentially, the model has "memorized" the training data rather than learning the underlying concepts, causing it to perform poorly on new data.

This phenomenon occurs when a model becomes overly complex, with a high number of parameters or features relative to the amount of training data available. In such cases, the model can exploit even the smallest variations in the training data, including outliers and noise, which are not representative of the true underlying relationships. Consequently, when presented with new data, the model's predictions are inaccurate and unreliable.

To combat overfitting, various techniques can be employed. Regularization methods, such as L1 and L2 regularization, add penalty terms to the model's optimization objective, discouraging the model from assigning excessively high weights to certain features. Cross-validation, a process where the data is split into training and validation sets multiple times, helps assess a model's generalization performance and can prevent overfitting by providing a more realistic measure of its effectiveness. Apart from it by obtaining a Machine Learning Certification, you can advance your career in  Machine Learning. With this course, you can demonstrate your expertise in designing and implementing a model building, creating AI and machine learning solutions, performing feature engineering, many more fundamental concepts, and many more critical concepts among others.

Feature selection and engineering are also crucial strategies. Removing irrelevant or redundant features and transforming data to expose more meaningful patterns can help the model focus on the essential aspects of the data. Another technique is to use simpler models with fewer degrees of freedom, like decision trees with limited depth or linear models, which are less likely to capture noise.

Ensemble methods, such as random forests and gradient boosting, combine predictions from multiple models to improve generalization and reduce overfitting. These methods leverage the diversity of individual models to provide more robust predictions. Moreover, collecting more data can be immensely helpful, as a larger dataset provides the model with a broader representation of the underlying patterns, making it harder for the model to overfit.

In conclusion, overfitting is a critical challenge in machine learning that occurs when a model becomes too complex and fits the training data too closely, leading to poor generalization performance on new data. Preventing overfitting involves employing techniques such as regularization, cross-validation, feature selection, and using simpler models, as well as leveraging ensemble methods and acquiring more data to ensure the model captures the true underlying relationships rather than noise and variations.