params.json

{"name":"Early-ml","tagline":"A Python step-by-step primer for Machine Learning and Optimization","body":"# Early-ML\r\n\r\n\r\n\r\n## Presentation\r\n\r\nGeneral Machine Learning tutorials\r\n\r\nA Python step-by-step primer for Machine Learning and Optimization\r\n\r\nThis `github` repository gathers `python` language training for Machine Learning and Optimization from basics of Python programming to Deep Learning.\r\n\r\n## Objectives\r\n\r\nSimple and step-by-step. One goal of `early-ML` is to show how to use some classical ML or data-related packages (such as `sklearn`) but also to have a deeper understanding of some ML algorithms where we use simple and plain Python to re-create our Machine Learning and Optimization routines.\r\n\r\n## Organization\r\n\r\nDepending on your Python level, best is to start to have a look at the organisation of the repository and pick the subject you are interested in.\r\n\r\n## Use\r\n\r\nStart with cloning the repository\r\n\r\n```bash\r\ngit clone https://github.com/dbetteb/early-ML.git\r\n```\r\n\r\nand then go to `early-ML` folder and jump on the subjects you want to get trained to.\r\n\r\n## Installation\r\n\r\nYou should have a Python 3.5+ installation working with the following packages\r\n- `numpy, scipy`\r\n- `pandas`\r\n- `scikit-learn`\r\n- `ipython`\r\n- `jupyter`\r\n- `plotly`\r\n\r\n## Why `early-ML` ?\r\n\r\nThere exists tons of training on Machine Learning with Python. However this ones focuses on early principles and explaination behind the scene. Many people figure they understand Gradient Boosted Machines for instance since they obtain good results with `xgboost` package for instance but they do not know about the machinery and the algorithms behind. `early-ML` will let you figure out about the algorithms !\r\n\r\n\r\n## Links\r\n\r\n\r\n\r\n","note":"Don't delete this file! It's used internally to help with page regeneration."}