Welcome to the May Edition of H2O Community Newsletter – this is the first of many meetup newsletters that makers of the H2O community have started curating to make sure we stay connected, continue learning, and are in the know of all the latest in AI and machine learning. We will start with a few things we think are worth noting and will continue building upon feedback as we progress. At the end of this newsletter, you will find a survey to give your feedback.


Top 5 Free books to understand Machine Learning:

  1. Introduction to Statistical Learning
  2. Neural networks and Deep Learning
  3. Pattern Recognition and Machine Learning 
  4. Deep Learning Book
  5. Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD (fastai book-Preview)


The latest release of H2O-3 (Zahradnik), is packed with new features and algorithms! H2O.ai has introduced support for Generalized Additive Models, added an option to build many models in parallel on segments of your dataset, improved support for deploying on Kubernetes, upgraded XGBoost with newly added features, improved import from secured Hive clusters, and vastly improved our AutoML framework. Do check it out here

In other open-source news, PyCaret caught our attention – an open-source machine learning library in Python to train and deploy supervised and unsupervised machine learning models in a low-code environment. You can check it out here.

Have you read the latest blog from the growth team at H2O.ai on the 5 best tips to ace virtual meetings? Read more about it here.

Exciting virtual meetups in May to tune in to:

In the interview of the week section, we listen to Sanyam Bhutani’s popular Chai Time Data Science show where he talks to Mark Landry about his data science journey, becoming a Kaggle Grandmaster, and more. You can catch the interview here.

We asked SRK for a pro-tip on starting out in data science and he had the following to say:

“First and foremost, data science enthusiasts must understand whether this field is of actual interest to them or they want to get associated with it just because of the hype around it. It is a rapidly evolving field and requires continuous learning, and so only passion will help sustain in the long run.

Once the person understands the basic concepts of machine learning either from courses or books, the crucial step is to get hands-on knowledge. There are multiple ways to do that, including participating in data science hackathons, contributing to open source projects, writing blogs, doing internships, etc. One can take up one or a few of the above skills to hone and showcase their talents.”

At H2O.ai, we offer H2O Driverless AI licenses to academics worldwide at no cost – please visit: https://www.h2o.ai/academic/

H2O meetup community expanded to the following regions this past month, please join a group near you to keep updated:


Lastly, help us in curating this newsletter to your liking – Take a quick survey


Thank You and stay safe! 

Team H2O.ai