data science

What's happening with AI today? (Ep. 164)

In this episode I have a wonderful chat with Ronald Schmelzer and Kathleen Walch, authors of "AI Today" the top podcast for those wanting a no-hype, practical, real-world insight into what enterprises, public sector agencies, thought leaders, leading technology companies, pundits, and experts are doing with AI today.
 
 
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Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.
 
 
Links
 
AI Today podcast: http://aitoday.live/
CPMAI Methodology: https://www.cognilytica.com/cpmai-methodology/
 
 

2 effective ways to explain your predictions (Ep. 163)

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References
Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. “Model Class Reliance: Variable importance measures for any machine learning model class, from the ‘Rashomon’ perspective.” http://arxiv.org/abs/1801.01489 (2018).
Python SHAP https://github.com/slundberg/shap
 
 

The Netflix challenge. Fair or what? (Ep. 162)

Remember the Netflix challenge?
It was a ton of money for the one who would have cracked the problem of recommending the best possible movie.
Was it a fair challenge? Did it work?Let me tell you what happened...
 
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GitHub Copilot: yay or nay? (Ep. 159)

It made already quite some noise in the news, GitHub copilot promises to be your pair programmer for life.In this episode I explain how and what GitHub copilot does. Should developers be happy, scared or just keep coding the traditional way?
 
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Pandas vs Rust [RB] (Ep. 158)

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A simple trick for very unbalanced data (Ep. 157)

Data from the real world are never perfectly balanced. In this episode I explain a simple yet effective trick to train models with very unbalanced data. Enjoy the show!
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References
Leo Breiman, Random Forests, 2001
C. Chen, A. Liaw, L. Breiman, Using Random Forest to Learn Imbalanced Data (2004)
 

Time to take your data back with Tapmydata (Ep. 156)

In this episode I am with Gilbert Hill, head of strategy at https://tapmydata.com/
We speak about personal data, blockchain and the ability to control it and monetize with another simple yet effective app in the ecosystem.
 
 
References
https://tapmydata.com/
https://medium.com/@tholder/we-dont-want-your-data-pushing-boundaries-in-data-collection-and-end-to-end-encryption-for-apps-ebd1d5f79df5

MLOps: the good, the bad and the ugly (Ep. 153)

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MLOps: what is and why it is important Part 2 (Ep. 152)

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MLOps: what is and why it is important (Ep. 151)

If you think that knowing Tensorflow and Scikit-learn is enough, think again.MLOps is one of those trendy terms today. What is MLOps and why is it important? In this episode I speak about the undeniable evolution of the data scientist in the last 5-10 years.
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Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.