inspiring

Machine learning algorithms can be black boxes--inputs go in, outputs come out, and what happens in the middle is anybody's guess.  But understanding how a model arrives at an answer is critical for interpreting the model, and for knowing if it's doing something reasonable (one could even say... trustworthy).  We'll talk about a new algorithm called LIME that seeks to make any model more understandable and interpretable.

Relevant Links:
http://arxiv.org/abs/1602.04938
https://github.com/marcotcr/lime/tree/master/lime

Model Interpretation (and Trust Issues)

In this episode of the SuperDataScience Podcast, I chat with Aspiring Data Scientist Nicholas Cepeda. You will be able to discover the many different pathways in Data Science, learn R from a SQL background and know about SAS Enterprise Miner. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/41

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 041: An Inspiring Journey from a Totally Different Background to Data Science

The year: 1998.  The size of the web: 150 million pages.  The problem: information retrieval.  How do you find the "best" web pages to return in response to a query?  A graduate student named Larry Page had an idea for how it could be done better and created a search engine as a research project.  That search engine was called Google.

Page Rank

In this episode of the SuperDataScience Podcast, I chat with one of the key people behind the Python package scikit-learn, Andreas Mueller. You will learn about gradient boosting algorithms, XGBoost, LightGBM and HistGradientBoosting. You will hear Andreas's approach to solving problems, what machine learning algorithms he prefers to apply to a given data science challenge, in which order and why. You will also hear about problems with Kaggle competitions. You will find out the four key questions that Andreas recommends to ask when you have a data challenge in front of you. You will learn about his 95% rule to creating models, and creating success in business enterprises with the help of machine learning. And, finally, you will also learn about the Data Science Institute at Columbia University.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/283

inspiring

Lakshmi Dhanesh