Starting with Colab

This is a little route one, but Google’s Colab is the place to start when coding.

The advantages are:
1) No setup required: You can run your Python code directly in your browser.
2) Easy sharing: It's integrated with Google Drive so you can share your work with others, and access your notebook from any device.
3) Support for various libraries and frameworks: Colab supports TensorFlow, PyTorch, Keras, and OpenCV. This makes it perfect for Machine Learning and Data Analysis tasks.
4) Safety and privacy: Your code is executed in a virtualized environment, ensuring safety. So, unless shared, others can't access your notebooks.

And the disadvantages:
1) Session limitations: There's a limit on the continuous computation time (around 12 hours for GPUs), after which the virtual machine you're using will be recycled. This can be problematic for long-running computations.
2) Resource limitations: The free version provides limited GPU/TPU memory. For larger models or datasets, this can be a bottleneck.
3) No persistent storage: Any data you store will be lost once your session is terminated. While integration with Google Drive can help, it's not as seamless as having persistent storage.