ml-stack-lite

A minimal friendly introduction to tech stack for getting started with Machine Learning and Deep Learning in Python targeted for absolute beginners

The course is structured to cover the following sections

1. Python
2. SQL
3. Pandas
4. Spark
5. Machine Learning
6. Deep Learning

Each section will be presented as notebooks, which will be self-contained and will also include exercises. You have to go through the notebooks and complete the corresponding exercises.

Python

The reading materials for Python basics can be found in /python/notebooks/ folder and assignments can be found in /python/exercises folder.

SQL

The reading materials for Python basics can be found in /sql/notebooks/ folder and assignments can be found in /sql/exercises folder.

This repository is pretty much a living one, the notebooks and exercises are updated as required. The material for the other sections will be added shortly. Also, please feel free to open a PR if you want to add/modify the course contents

Getting started with learning materials

You can clone this repository and launch jupyter and access the notebooks

OR

You can use Google Collab (a simple one click solution from Google) to read and playaround with the learning materials/notebooks. We recommend you to use this setup, to avoid any version or installation problems. To Open a notebook in this GitHub Repository in Google Collab:

• Open Google Collab and choose GITHUB tab
• Copy the GitHub link to the notebook that you want to open
• Paste this Github link in Google Collab and hit the Search Icon
• It shows the notebook's name along with the name of the repository and current branch.
• Click on the notebook's name to get started

Getting started with assignments

To work on the assignments in your local machine, make sure you have completed these steps

• Create a conda environment
• Setup Pycharm project (using the conda environment)