Kansas, Michigan State, Gonzaga or Dayton? Which team had the best chance at winning March Madness 2020? YOU can find out with your machine learning skills.
March Madness and the annual Kaggle competition have both been cancelled. But that doesn't mean we can't use Machine Learning to make a prediction!
Follow along as we work through Kaggle's historical datasets, pick out key stats, and apply suitable algorithms. Then try it yourself, using our included March Madness Runtime Environment, which will get you up and running quickly. Learn how to run your own 2020 March Madness Mock tournament using common ML techniques so you'll be all set to win the Kaggle competition next year.
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No time to try out the ActiveState Platform? Now you can create a build just by uploading the requirements.txt file for your Python project, and let the Platform take care of the rest. To give it a try, just install the State Tool:
sh <(curl -q https://platform.activestate.com/dl/cli/install.sh)
And then use the following command to upload your requirements.txt file and kick off your build:
state package import [filename]
That's it! Just kick back and wait for the email notification.
Waiting for a Key Package?
Maybe you haven't joined the Platform yet because our package catalog doesn't have what you need yet. Good news! We've just added yet another 20,000 packages to our Python catalog, effectively bringing ~90% of the packages that matter in the Python ecosystem to the ActiveState Platform. Highlights this time around include moto (for mocking AWS services) and JupyterLab (the web-based UI for Jupyter notebooks), both of which are featured in this week's blog on How to Build a Data Pipeline. Still can't find the package you need? Let us know!
Simplify Windows CI/CD Pipelines
One of the great reasons to become an ActiveState Platform user is to resolve reproducibility and consistency issues with runtime environments that can cause build issues throughout the CI/CD chain. The ActiveState Platform's State Tool solves many of the reproducibility issues that plague builds, and ensures that all dependencies are always accounted for -- and it does it all without introducing a new artifact for you to manage. Read about it here.
The Keys to Machine Learning
Want to get started with machine learning, but don't know where to start? Download our "Mini ML Runtime" for Windows and Linux. It's a great way to kickstart learning and working with the growing field of machine learning since it includes everything you need to get started on projects that range from simple interactive dashboards to building your own digital virtual assistant -- all of which are available in our blogs.
A collection of interesting news and cool projects from around the web.
Want to brush up on your database knowledge? Check out this tutorial which explores the different Python SQL libraries and how to use them.
Artificial intelligence is increasingly finding its way into pharma and life sciences. Learn how AI can be used for disease identification, personalized treatment, research, and more.
Python is one of the world's most popular, in-demand programming languages. Check out this handy A-Z list of useful tips and tricks that you might be unaware of.
TensorFlow vs PyTorch. Which one to use in your next project?
One was originally developed by Google, the other by Facebook. While both are commonly used for creating and working with neural networks, co-founder and CTO at Skcript, Swaathi Kakarla really drills down and compares the two strategically. Understand which one will be most suitable for your next machine learning or data science project and why. Practice along with the GitHub examples!
You are receiving this email because you signed up for the latest insights from ActiveState. Code for thought by ActiveState is a monthly email that shares a topic handpicked for you by our dev content team.
