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.

Recently I bought a new Mac. I needed to install everything necessary to build a machine learning project in my local machine. I could have copied everything from my old Mac using Time Machine. However, I wanted to start from scratch so that I can document the steps. This blog is for those whose daily job is not coding but who wants to get their 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 on my machine.

Setting up Mac Machine Learning

  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. We can install those packages in the virtual environments 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
    Guide for Installing 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. Stay updated on our blog for more insights on Machine Learning, Product Management, and building digital products. 

About Leapfrog

Leapfrog is a full-stack technology services company that specializes in SaaS products, Web and Mobile Applications, and AI.
Our world-class teams have capabilities such as product design ( UI and UX), Front End and Back End Engineering, DevOps Engineering, Product Management, Data Engineering, and Machine Learning.


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