Advanced Data Science & Machine Learning

This will be a more advanced exploration of the libraries that developers will have been exposed to, deep learning, additional models, operationalization concerns of these systems, and the often-forgotten security aspects of data-driven systems.

The goal will be to have as many hands-on activities as is feasible intermixed with a sufficient amount of theory to motivate the exercises.

Curriculum:

• Deeper Data-Engineering Dives
• SQL
• Advanced Numpy
• Advanced Pandas
• Advanced Visualization
• Deeper Deep Learning Dives
• Keras and PyTorch
•  CNNs
•  Generative Models
•  PyTorch Lightning
• Model Exchange with ONNX
• Additional Models
•  Time Series Data
• Classical Time Series Models
• RNNs, LSTMs
• PyTorch Forecasting
• Anomaly Detection
•  Ranking Models
• Metrics
• Models
•  Multi-Modal Models
• Representation
• Alignment
• Composaibility of Models
• Modality Transference
• Quantification
• Multimodal Framework (MMF)
•  NLP Models
• Embeddings
• Transformers
• LMM and ChatGPT
• MLOps and Security
• Data Engineering Pipelines
• Apache Airflow
• Deployment Architectures
• Continuous Learning
• Handling Streaming Data
• Security
• Threat Taxonomies
• Monitoring, Logging, and Alerting