Apply unsupervised learning methods, such as dimensionality reduction, manifold learning, and density estimation, to transform and visualize data. 

Understand, evaluate, optimize, and correctly apply clustering algorithms using hierarchical, partitioning, and density-based methods.

Use topic modeling to find important themes in text data and use word embeddings to analyze patterns in text data. 

Manage missing data using supervised and unsupervised imputation methods, and use semi-supervised learning to work with partially-labeled datasets.

Understand the fundamental principles underlying network structures and apply NetworkX to analyze these principles in real-world networks.

Describe the practical uses of the community detection problem and use algorithms to detect and evaluate community structure in real networks.

Explain the value and applications of network generation models, learn their limits and strengths, and employ them to create synthetic networks.

Identify several basic diffusion models and implement them to run simulations using real and synthetic networks.

Develop skills to process and interpret information presented in free-text data.

Identify the major classes of named entity recognition (NER) and implement, with guidance, state-of-the-art machine learning techniques for NER.

Compare, contrast, and select between multiple machine learning and deep learning approaches for NER.

Explore Large Language Models and configure a Transformer-based pipeline to extract entities of interest from a text dataset.