Lesson 1 : Setting up Google Colab & Pre requisite Libraries
Google Colab [Google Colaboratory], is a popular cloud-based platform provided by Google that allows users to write, run, and share Python code using Jupyter notebooks in a collaborative environment. It’s a valuable tool for data scientists, machine learning engineers, researchers, educators and students offering several key features:
Free Access: Google Colab is free to use and provides access to a high-powered computing environment, including GPU and TPU (Tensor Processing Unit) support, without the need for expensive hardware.
Jupyter Notebooks: Colab supports Jupyter notebooks, which are interactive documents that combine code, text, and visualizations, making it easy to document and share your work.
Cloud-Based: Since it’s hosted in the cloud, there’s no need to install any software on your local machine. You can access your notebooks from any device with an internet connection.
Collaboration: It enables real-time collaboration with others, similar to Google Docs. You can share your notebooks with colleagues or friends, allowing them to view or edit your work.
Pre-installed Libraries: Colab comes with many popular Python libraries pre-installed, including NumPy, Pandas, Matplotlib, and TensorFlow, which is particularly useful for data analysis and machine learning tasks.
Hardware Acceleration: Users can take advantage of free GPU and TPU resources provided by Google, which significantly speed up computations for tasks like training deep learning models.
Data Integration: It offers seamless integration with Google Drive, allowing you to access and store data and notebooks directly in your Google Drive account.
Overall, Google Colab is a versatile and accessible platform that empowers users to develop, experiment with, and share code and data-driven projects, making it a valuable tool in the fields of data science, machine learning, and research.