This lecture is focused on the following concepts:

1. Introduction the Python programming language;
2. Data wrangling using Pandas;
3. Applied mathematics using NumPy;
4. TODO: Understand linear models;
5. Understand tree-based algorithms;
6. Evaluate a machine-learning model;
7. Manage mixed data types in machine-learning pipeline;
8. Fine tuning model by hyper-parameters search.

Some intro slides: http://ogrisel.github.io/decks/2017_intro_sklearn

In case that you have any issues, you click on the binder link below which will setup an online machine for you:

Alternatively you can create a new conda environment which will be called dsc by default and whill contain all the packages required to run the notebooks:

conda env create -f environment.yml

conda activate dsc
cd path/to/datascience_started_course
jupyter notebook

You can also update an existing conda environment:

conda env update -f environment.yml

This material is inspired and reused part of the following materials:

• https://github.com/amueller/scipy-2018-sklearn
• https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial
• https://github.com/INRIA/scikit-learn-mooc