This Week in Machine Learning: Girl Power, Methods for Newbies, The Best Books & More​

Weekly roundup in machine learning
Every day interesting things happen in the world of Data Science. People unveil new, unknown secrets of machine learning so we can learn more about the wonders of technology. What has happened in machine learning over the last week? We’ve gathered the most interesting stories. Here goes a dose of the latest news, discoveries, and inspiring stories. There is something for everyone. Enjoy your read!

Weekly Roundup: March 3rd - 8th

Doing machine learning the right way by Rob Matheson | MIT News Office, March 7, 2020

A fascinating story on how professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.

> Best Machine Learning Books (Updated for 2020) Alessio Gozzoli | March 3

If you enjoy reading, you will find this article helpful. The best positions you should read on machine learning.

> Step-By-Step Framework for Imbalanced Classification Projects by Jason Brownlee, March 9

A detailed tutorial on a systematic framework for working through an imbalanced classification dataset. An interesting read for those who want to learn techniques specifically designed for imbalanced classification.

> 20 women doing fascinating work in AI, machine learning and data science by Elaine Burke, March 9

Here’s a little inspiration for all the ladies (and gentlemen) working in the ML field.

> Best 13 Machine Learning Methods And Techniques For Newbies by Nasir Hawlader, March 7

If you’re new to the world of machine learning and not sure which way to go, this article will be your guide.

> KDnuggets™ News of the week with top stories and tweets of the past week, plus opinions, tutorials, events, webinars, meetings, and jobs

> Don’t forget about the reliable Reddit thread on ML for more news on machine learning!

That’s all folks! I hope you found something of interest in this weekly roundup. Don’t forget to check our blog for more inspiring articles.

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