In this show I interview Sebastian Raschka, data scientist and author of Python Machine Learning.
In addition to the fun we had offline, there are great elements about machine learning, data science, current and future trends, to keep an ear on. Moreover, it is the conversation of two data scientists who contribute and operate in the field, on a daily basis.
This episode is guaranteed to have great insights.
- Blog article: http://sebastianraschka.com/
Large-scale virtual screening project (finding a sea lamprey pheromone receptor antagonist)
- Poster: http://sebastianraschka.com/
- Recent news about our project (Sea Lamprey Mating Pheromone Registered By U.S. Environmental Protection Agency As First Vertebrate Pheromone Biopesticide): https://t.e2ma.net/message/
- TPOT – Machine Learning Pipeline Optimization
- TPOT GitHub Repo: https://github.com/rhiever/
- Singh, Aarti, Robert Nowak, and Xiaojin Zhu. “Unlabeled data: Now it helps, now it doesn’t.” Advances in neural information processing systems. 2009.
- P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2005. http://www-users.cs.umn.edu/~
- Sebastian Raschka. Python Machine Learning. Packt Publishing Ltd.
- T. Hastie, R. Tibshirani, J. Friedman, T. Hastie, J. Friedman, and R. Tibshirani. The Elements of Statistical Learning, volume 2. Springer, 2009. http://statweb.stanford.edu/~
- C. M. Bishop et al. Pattern recognition and machine learning, volume 1. springer New York, 2006.
- Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012.
- Pedro Domingos. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. New York: Basic Books, 2015.