Renee interviews computational biologist, author, data scientist, and Michigan State PhD candidate Sebastian Raschka about how he became a data scientist, his current research, and about his book Python Machine Learning. In the audio interview, Sebastian also joins us to discuss k-fold cross-validation for our model evaluation Data Science Learning Club activity.
Podcast Audio Links:Link to podcast Episode 8 audioPodcast’s RSS feed for podcast subscription appsPodcast on StitcherPodcast on iTunes
Podcast Video Playlist:Youtube playlist of interview videos
More about the Data Science Learning Club:Data Science Learning Club Welcome MessageLearning Club Activity 8: Evaluation Metrics [coming soon]Data Science Learning Club Meet & GreetNow sponsored by DataCamp!
Links to topics mentioned by Sebastian in the interview:
computational biology
molecular docking
Protein-ligand docking
DNA -> RNA -> protein
protein signaling pathways
graph theory
Ensemble learning
cost function
fitness function
ligand and binding affinity
sea lamprey
pheromone
SiteInterlock project
Neural Network
Random Forest
Sebastian’s Python Machine Learning repository on GitHub
Python Machine Learning Book on DataSciGuide
scikit-learn – Voting Classifier
softmax regression
stochastic gradient descent
multilayer perceptron
logistic regression (from Sebastian’s github)
regularization in logistic regression (from Sebastian’s github)
Keras deep learning library
@rasbt on TwitterSebastian Raschka on Quora
Sebastian’s book on Amazon:
