Hands-on Data Science and Machine Learning Training (2020)

Dive into a comprehensive hands-on training series that provides practical experience with data science and machine learning techniques for research and educational applications. Master the fundamentals of Jupyter notebooks for research workflows, learn to interact with data repositories and implement effective data management strategies. Develop skills in training neural networks and random forests for supervised learning tasks, apply unsupervised learning techniques including Principal Component Analysis for dimensionality reduction, and explore sequential learning methods for Design of Experiments using machine learning approaches. Gain practical experience with data organization, plotting, linear regression, classification algorithms, and matrix decomposition techniques through cloud-based exercises that require no software installation. Access all training materials and continue research work using nanoHUB's cloud computing resources, making this series ideal for active researchers and educators regardless of prior coding experience.