Beginner's guide to machine learning with Scikit-Learn

Just like cloud computing ushered in the current explosion in startups … machine learning platforms will likely power the next generation of consumer and business tools.

We are living in a world where we are seeing vast advancements in ML and AI thanks to the democratization of machine learning, a form of artificial intelligence that enables computers to learn from data, without being explicitly programmed. With numerous online resources available, one often suffers from what right combination of tools to use to get started. This project aims to take away that friction and get you started with machine learning in minutes.

We will be using the following:

Scikit-Learn is an open source machine learning library built on NumPy, SciPy, and matplotlib which provides powerful tools for data mining and data analysis.
Docker is an awesome tool that you should have learnt yesterday. It makes setting up the development environment a breeze.
Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations and explanatory text, right in your browser.

To get started

Install Docker
Clone this project

git clone git@github.com:sud218/ml-scikit-boilerplate.git
cd ml-scikit-boilerplate

Run make and you are done!

make

You should see the following with the link to your notebook. Grab that link and paste into your browser.

Voila! You are ready to get started on machine learning.

Note: If you are running on cloud, replace the ip 0.0.0.0 in the above url to with your docker-ip. You can very easily get your docker-ip by running make docker-ip.

What's next?

Read Scikit docs and explore different models and tools available.
Create your own notebook!

Facing trouble running? Please create an issue and I will get back to you.