The median is the middle value in a dataset when it's ordered. It's a measure of central tendency that's not affected by extreme values (outliers). To find the median, you arrange the data in ascending order, and if there's an even number of values, it's the average of the two middle values.

Hybrid recommender systems leverage both collaborative filtering (user-user/item-item) and content-based methods to provide more accurate recommendations. Collaborative filtering focuses on user behavior, while content-based filtering considers item attributes.

The T-test for independent samples is used to determine if there is a significant difference between the means of two independent groups. It is commonly employed in hypothesis testing to compare means. The chi-squared test is used for testing the independence of categorical variables, ANOVA for comparing more than two group means, and the correlation test for measuring the strength and direction of a linear relationship.

The Convolutional layer in Convolutional Neural Networks (CNNs) is responsible for detecting local features in the input data, such as edges and textures. It does this by applying convolution operations across the input data, which allows the network to recognize spatial patterns in images or other structured data.

Collaborative Filtering is a recommendation approach that identifies users similar to the target user based on their interactions and recommends items liked by those similar users. It relies on user-user similarity for recommendations.

Linear Regression is a commonly used algorithm for predicting continuous target variables. It establishes a linear relationship between the input features and the target variable, making it suitable for tasks like price prediction or trend analysis in Data Science.

In a data warehouse, the "Fact" table is used to store aggregated data at various levels of granularity. These tables contain measures or metrics, which are essential for analytical queries and business intelligence reporting.

Apache Flume is primarily used in the Hadoop ecosystem for data ingestion from various sources. It is a distributed, reliable, and available system for efficiently collecting, aggregating, and moving large amounts of data to Hadoop's storage or other processing components. Flume is essential for handling data ingestion pipelines in Hadoop environments.

Min-Max normalization can be sensitive to outliers. If outliers are present in the data, this scaling method can compress the majority of data points into a narrow range, making it less suitable for preserving the information in the presence of outliers. In scenarios where outliers are a concern, alternative scaling methods like Robust Scaling may be preferred.

Serialization is the process of converting a trained machine learning model into a format that can be used by production systems. It involves saving the model's parameters, architecture, and weights in a portable format so that it can be loaded and utilized for making predictions in real-time applications.