End-to-End ML Project- Beginner friendly

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Virtual Environment

Now, let's come to the second part of the pipeline 'Get the data'. When you are learning about machine learning, it is best to actually experiment with real-world data.

It is always advised to create a virtual environment for every project so that no conflict regarding any dependencies or library versions occurs.

Suppose you have worked on a project in the past using tensorflow 1.4.1. Now you are working on another project and use tensorflow 2.8.0 in it. So you will update the tensorflow version from 1.4.1 to 2.8.0 to work on the new project. But now when you try to run the previous project, it will most probably raise errors as this project was built on tensorflow 1.4.1 and the current version installed in your system is tensorflow 2.8.0.

So, now you will have to update your whole project w.r.t the new version. Instead, it would be more advisable to create a separate environment for the new project so its dependencies don't create problems for other projects. This separate environment is called a virtual environment.

So, now you can install tensorflow 2.8.0 in this virtual environment and it will not affect other projects as in the other environment there will still be tensorflow 1.4.1 installed.

But you don't have to create one here as you do not need to install any libraries because they are pre-installed in our lab


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