This repository contains some tutorials I programmed to learn more about MachineLearning topics as well as some basic concepts in the same area. I hope you find it useful. Currently I am busy creating a ML framework to be able to react faster to requests and competitions. More about this soon...
Kaggle is a good place to learn machine learning and data science. I think its docker image is a good option for data science development environment for the following scenarios:
- if you want to do local experiments for Kaggle’s kernel-only competitions, this is exactly the same environment of Kaggle kernels
- for everything else, the list of packages included in the image is built by the Kaggle community and therefore includes most useful tools that one know or don’t know about; this is much easier than building your own list of packages.
This Dockerfile here contains the environment which is used and proposed by kaggle for general competitions. If you don't want to use the "all-round" variant, I have also created some yaml files in the subfolders, which only contain the minimal config of required libraries.
You can find additional information on how to use Docker, Containers, Images and Dockerfiles in this section of my repositories: Links to Docker
More about this soon, too...
800 machine learning components
project based learning (python)
Some tutorials can be found in this section
Notebook with some basic statistical visualizations with the help of some libraries like seaborn, matplot or plotly
How to do some basic operation with dataframes
Some NLP examples