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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.