How do you keep your feature engineering skills up to date and learn from others?

1 Read blogs and books

Learning from experts and peers is possible through reading blogs and books on feature engineering. Online resources can provide insight on topics like feature selection, extraction, scaling, encoding, and interaction. Additionally, real-world case studies and best practices can be studied. Examples of blogs and books include Feature Engineering for Machine Learning by Alice Zheng and Amanda Casari, Feature Engineering and Selection by Max Kuhn and Kjell Johnson, The Feature Engineering Blog by HJ van Veen, and the Machine Learning Mastery Blog by Jason Brownlee.

2 Watch podcasts and videos

Staying up to date and learning from others can be achieved by watching podcasts and videos on feature engineering. You can listen to interviews, tutorials, lectures, and presentations from experts and practitioners, as well as ask questions and join discussions. Examples of such podcasts and videos include the Data Engineering Podcast by Tobias Macey, the Feature Labs Podcast by Feature Labs, DataCamp Live Coding Sessions by DataCamp, and Feature Engineering for Machine Learning by Google Cloud.

3 Practice on datasets and competitions

A third way to stay informed and learn from others is to practice on datasets and competitions. This allows you to apply your knowledge and creativity to solve real-world problems and challenges, as well as compare your results and methods with others and receive feedback. Examples of datasets and competitions include Kaggle Datasets and Competitions by Kaggle, UCI Machine Learning Repository by UCI, OpenML Datasets and Tasks by OpenML, and DrivenData Competitions by DrivenData.

4 Join communities and networks

Staying up to date with your skills and learning from others can be achieved by joining communities and networks dedicated to feature engineering. Connecting with other enthusiasts and professionals allows you to share ideas and experiences, as well as benefit from their insights and tips. Additionally, you can find opportunities and collaborations through these networks. Examples of such networks include the Feature Engineering Slack Channel by Feature Labs, the r/featureengineering Subreddit by Reddit, the Feature Engineering LinkedIn Group by LinkedIn, and the Machine Learning Feature Engineering Meetup by Meetup.

5 Take courses and workshops

A fifth way to stay current and learn from others is to take courses and workshops on feature engineering. Structured and interactive curricula, instructors, and mentors can provide valuable insight, as well as certificates and credentials. For example, DataCamp offers Feature Engineering for Machine Learning in Python, Coursera has a Feature Engineering for NLP in Python course, Pluralsight offers Advanced Feature Engineering in Python, and O'Reilly has a Feature Engineering Workshop.

6 Experiment with tools and frameworks

A sixth way to keep your skills up to date and learn from others is to experiment with tools and frameworks on feature engineering. Automating, simplifying, and optimizing the feature engineering process can be done using state-of-the-art techniques and algorithms. Additionally, exploring new possibilities and innovations is also possible. Examples of tools and frameworks are Featuretools by Feature Labs, Scikit-learn by Scikit-learn, PySpark by Apache Spark, and TensorFlow Transform by TensorFlow.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?