Colab, short for Google Colaboratory, is a cloud-based platform for machine learning and data analysis that provides a free and easy way to get started with powerful computing resources. Colab provides a Jupyter notebook interface that allows you to write and run Python code in your browser, and includes access to a wide range of popular libraries and frameworks for data analysis and machine learning.

Colab is incredibly versatile and can be used for a wide range of applications. It's especially useful for businesses that want to explore data, build machine learning models, and prototype new projects without needing to invest in expensive hardware or infrastructure. With Colab, you can easily share your work with colleagues and collaborators, and even run your notebooks on Google's powerful cloud infrastructure for more intensive workloads.

Underneath the hood, Colab uses Google's cloud infrastructure to provide you with powerful computing resources, including CPUs, GPUs, and TPUs. This means you can run complex machine learning models and data analyses without worrying about hardware limitations or performance issues.

Colab is an incredibly useful tool for businesses that want to get started with data analysis and machine learning, or for more experienced users who want to prototype and experiment with new projects. With its ease of use, powerful computing resources, and ability to collaborate and share work, Colab is an excellent choice for any organisation looking to unlock the power of data science.

If you are interested in learning more, you can see the study guides I used to practice ML here.