Models like BERT (Bidirectional Encoder Representations from Transformers) have gained popularity in NLP for their pre-trained weights. These models can be fine-tuned for various downstream tasks, saving time and resources and achieving state-of-the-art results.

Seaborn is a Python data visualization library that provides the FacetGrid class for creating complex layered visualizations by combining multiple plots. It allows you to create grid-like structures of subplots to visualize relationships between variables in your data, making it ideal for advanced visualization tasks.

Machine Learning Engineers are responsible for developing and deploying machine learning models into production systems. They work closely with Data Scientists who create the models but specialize in the deployment process.

YOLO (You Only Look Once) is a popular algorithm for real-time object detection. It efficiently detects objects in images or videos, making it suitable for various applications, including self-driving cars and surveillance.

RNNs (Recurrent Neural Networks) are known for their ability to retain memory from previous inputs in a sequence, making them effective for tasks like speech recognition, where the order of input data and contextual information is crucial for accurate prediction. Speech recognition relies on capturing temporal dependencies in audio data, which RNNs excel at.

Raw logs from web servers often contain unstructured data, as they can consist of a mix of text, images, and various file types that lack a specific format. Unstructured data is not organized in a traditional tabular structure.

Sentiment Analysis is an NLP task that involves determining the emotional tone or sentiment behind a series of words, often classifying it as positive, negative, or neutral. It's crucial for understanding public opinion and customer feedback.

Convolutional Neural Networks (CNNs) are specifically designed for image classification tasks. They use convolutional layers to capture spatial hierarchies in the input data, making them highly effective for image recognition and analysis.

Supervised Learning with Linear Regression is appropriate for predicting a continuous target variable (spending amount) based on historical data. Unsupervised learning is not suitable for prediction tasks, reinforcement learning is for sequential decisions, and semi-supervised learning combines labeled and unlabeled data.

The initial step in the Data Science Life Cycle is problem formulation. In this step, the objectives are defined, and the problem statement is understood. It sets the direction for the entire data science project.