The primary objective of feature scaling is to make features with different scales or units compatible so that machine learning algorithms, particularly those based on distance metrics, are not biased towards features with larger scales. This ensures that each feature contributes equally to the model's performance. Improving interpretability and visualization may be secondary benefits of feature scaling, but the main goal is compatibility.

Semi-structured data often contains a mix of structured tables and unstructured text. It's a flexible data format that can combine organized data elements with more free-form content, making it suitable for a wide range of data types and use cases, such as web data and NoSQL databases.

Convolutional Layers in neural networks are responsible for feature extraction and learning spatial hierarchies, making them crucial in tasks such as image recognition. They apply filters to the input data, capturing different features.

In transfer learning, a model trained on a large dataset is used as a starting point, and the knowledge gained is transferred to a new, similar task. This leverages the pre-trained model's knowledge to improve performance on the new task, particularly when the tasks are related.

Residual Networks (ResNets) are known for their residual connections, which allow for easier training of very deep networks. ResNets have become a standard in deep learning due to their ability to mitigate the vanishing gradient problem, enabling the training of much deeper architectures.

When working with large datasets that do not fit into memory, the Python library "Dask" is a useful tool for efficient computations. Dask provides parallel and distributed computing capabilities, enabling data scientists to handle larger-than-memory datasets using familiar Python tools.

In time-series analysis, creating lag features means using previous time steps (observations) as new data points. This allows you to incorporate historical information into your model, which can be valuable for forecasting future values in time series data.

A Box Plot is a commonly used visualization for detecting outliers in a single variable. It displays the distribution of data and identifies potential outliers based on the interquartile range (IQR). Data points outside the whiskers of the box plot are often considered outliers. Box plots are useful for identifying data anomalies.

Data Preprocessing is the step in the Data Science Life Cycle that involves cleaning the data, handling missing values, and preparing it for analysis. This step is crucial for ensuring the quality and reliability of the data used in subsequent analysis.

The mean, also known as the average, is a common measure of central tendency. It's calculated by adding up all the values in the dataset and then dividing by the number of data points. The mean provides a sense of the "typical" value in the dataset.