Handling Missing Data in Python/Pandas
Key Takeaways: It’s important to describe missing data and the challenges it poses. You need to clarify a confusing terminology that further adds to the field’s complexity. You should take the time to review methods for handling missing data. You need to learn how to apply robust multiple imputation... Read more
Thomas Wiecki of Quantopian on ‘Minding the Gap’ Between Statistics and Machine Learning at ODSC Europe 2018
Key Takeaways: It’s important for data scientists to understand the so-called “gap” between statistics and machine learning, and how there actually is a lot of commonality between the two; it’s just a matter of how you look at things. PyMC3 is a very useful probabilistic programming framework for Python.... Read more
Active Learning: Your Model’s New Personal Trainer
First, some facts. Fact: active learning is not just another name for reinforcement learning; active learning is not a model; and no, active learning is not deep learning. What active learning is and why it may be an important component of your next machine learning project was the subject... Read more