Setting up your Mac for Machine Learning

From zero to running the first Machine Learning and Deep Learning projects within hours using scikit-learn, Keras, and TensorFlow

I bought a new Mac. I need to install everything necessary to build the Machine Learning project on my local machine. I could have copied everything from my old Mac using Time Machine. However, I wanted to start from the scratch so that I can document the steps and I can share with all of those who daily job is not coding but who wants to get hands dirty now and then.

I am not a developer. I am a Product Manager. I don’t code daily, but it’s fun to write some code when necessary. At least, I can clone a project locally in my machine.

1. Install Python
   Check whether there is Python installed by default. Mac comes with default Python version 2.7.0. I will use Anaconda for Python and different packages for Data Science and Machine Learning.

2. Install Miniconda
   I chose Miniconda because I don’t need all the packages right away. I can install those packages in the virtual environments I create on the need basis. I like to isolate dependent packages for different types.

   • Anaconda vs Miniconda vs Virtual env
   • How to install Miniconda

3. Managing environments

   • Why you need environments
   • Conda documentation

4. Install Jupyter Notebook
   Install Jupyter Notebook
   How do you install packages in Jupyter notebook? There are complexities we need to be careful. If you are interested, then here is a good article.

5. After you install Jupyter Notebook, you can follow this notebook to install and check all those packages.

Hope you find this curation useful!

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Now that your Macbook is ready, want to take the next leap and learn more about integrating AI in your product?

This article was first published on lftechnology blog