bootcamp_machine-learning

Bootcamp Machine Learning

One week to learn the basics in Machine Learning! :robot:

Table of Contents

• Download
• Curriculum
  • Module05 - Stepping Into Machine Learning
  • Module06 - Univariate Linear Regression
  • Module07 - Multivariate Linear Regression
  • Module08 - Logistic Regression
  • Module09 - Regularization
• Acknowledgements
  • Contributors
  • Beta-testers

This project is a Machine Learning bootcamp created by 42 AI.

As notions seen during this bootcamp can be complex, we very strongly advise students to have previously done the following bootcamp:

• Python

42 Artificial Intelligence is a student organization of the Paris campus of the school 42. Our purpose is to foster discussion, learning, and interest in the field of artificial intelligence, by organizing various activities such as lectures and workshops.

Download

The pdf files of each module can be downloaded from our realease page: https://github.com/42-AI/bootcamp_machine-learning/releases

Curriculum

Module05 - Stepping Into Machine Learning

Get started with some linear algebra and statistics

Sum, mean, variance, standard deviation, vectors and matrices operations.
Hypothesis, model, regression, loss function.

Module06 - Univariate Linear Regression

Implement a method to improve your model's performance: gradient descent, and discover the notion of normalization

Gradient descent, linear regression, normalization.

Module07 - Multivariate Linear Regression

Extend the linear regression to handle more than one features, build polynomial models and detect overfitting

Multivariate linear hypothesis, multivariate linear gradient descent, polynomial models.
Training and test sets, overfitting.

Module08 - Logistic Regression

Discover your first classification algorithm: logistic regression!

Logistic hypothesis, logistic gradient descent, logistic regression, multiclass classification.
Accuracy, precision, recall, F1-score, confusion matrix.

Module09 - Regularization

Fight overfitting!

Regularization, overfitting. Regularized loss function, regularized gradient descent.
Regularized linear regression. Regularized logistic regression.

Acknowledgements

Contributors

• Amric Trudel (amric@42ai.fr)
• Maxime Choulika (maxime@42ai.fr)
• Pierre Peigné (ppeigne@student.42.fr)
• Matthieu David (mdavid@student.42.fr)
• Benjamin Carlier (bcarlier@student.42.fr)
• Pablo Clement (pclement@student.42.fr)
• Amir Mahla (amahla@42ai.fr)
• Mathieu Perez (mathieu.perez@42ai.fr)

Beta-testers

• Richard Blanc (riblanc@student.42.fr)
• Solveig Gaydon Ohl (sgaydon-@student.42.fr)
• Quentin Feuillade--Montixi (qfeuilla@student.42.fr)