Branch-AI/piscine/week02/day01/README.md

W2D01 Piscine AI - Data Science

Linear regression with Scikit Learn

The goal of this day is to understand practical Linear regression and supervised learning.

The word "regression" was introduced by Sir Francis Galton (a cousin of C. Darwin) when he studied the size of individuals within a progeny. He was trying to understand why large individuals in a population appeared to have smaller children, more close to the average population size; hence the introduction of the term "regression".

Today we will learn a basic algorithm used in supervised learning : The Linear Regression. We will be using Scikit-learn which is a machine learning library. It is designed to interoperate with the Python libraries NumPy and Pandas.

We will also learn progressively the Machine Learning methodology for supervised learning - today we will focus on evaluating a machine learning model by splitting the data set in a train set and a test set.

Exercises of the day

• Exercise 1 Scikit-learn estimator
• Exercise 2 Linear regression in 1D
• Exercise 3 Train test split
• Exercise 4 Forecast diabetes progression
• Bonus: Exercise 5 Gradient Descent - Optional

Virtual Environment

• Python 3.x
• NumPy
• Pandas
• Matplotlib
• Scikit Learn
• Jupyter or JupyterLab

Version of Scikit Learn I used to do the exercises: 0.22. I suggest to use the most recent one. Scikit Learn 1.0 is finally available after ... 14 years.

Ressources

To start with Scikit-learn

• https://scikit-learn.org/stable/tutorial/basic/tutorial.html

• https://jakevdp.github.io/PythonDataScienceHandbook/05.02-introducing-scikit-learn.html

• https://scikit-learn.org/stable/modules/linear_model.html

Machine learning methodology and algorithms

• This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Andrew Ng is a star in the Machine Learning community. I recommend to spend some time during the projects to focus on some algorithms. However, Python is not the language used for the course. https://www.coursera.org/learn/machine-learning

• https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet

• https://scikit-learn.org/stable/tutorial/index.html

Linear Regression

• https://towardsdatascience.com/laymans-introduction-to-linear-regression-8b334a3dab09

• https://towardsdatascience.com/linear-regression-the-actually-complete-introduction-67152323fcf2

Train test split

• https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/

• https://developers.google.com/machine-learning/crash-course/training-and-test-sets/video-lecture?hl=en