Mastering Machine Learning – Ep.1: Imputing Missing Values With Scikit-learn
***It is no longer possible to register for this webinar but you can watch it on-demand here***
***GitHub repo: https://github.com/cloudacademy/ca-webinars-mastering-machine-learning***
Approximately 90% of any data scientist’s day is taken up by cleaning data. More often than not, data sets are incomplete or incorrect, forcing people to lose a significant amount of time taking corrective actions and hindering efficiency. We will look at a processing technique called Imputation that allows one to retrieve unknown data.
This session will cover:
– Simple Imputer
– Iterative Imputer
– KNN-Imputer
On top of this session, you can also sign up for the upcoming webinars of this series by clicking on the links below:
- October 14th | Mastering Machine Learning – Ep.2: Build and Run a Machine Learning Pipeline
- November 4th | Mastering Machine Learning – Ep.3: Be a Data Science Master and Commander with XGBoost
- November 18th | Mastering Machine Learning – Ep.4: Build a Sentiment Analysis Pipeline
- December 9th | Mastering Machine Learning – Ep.5: Natural Language Processes Using Amazon Sagemaker
– All registrants will receive the recording of the webinar via email
Andrea Giussani, Data Scientist at Cloud Academy
With a Ph.D. in Statistics, Andrea is a full-time Data Scientist at Cloud Academy. On top of his day to day job, he is also an Academic Fellow in Computer Science at Bocconi University in Milan. He is also a published author, check out his bestseller Applied Machine Learning with Python.
