Bootcamp Machine Learning
One week to learn basics in Machine Learning! 🤖
Table of Contents
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:
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 ([email protected])
- Maxime Choulika ([email protected])
- Pierre Peigné ([email protected])
- Matthieu David ([email protected])
- Benjamin Carlier ([email protected])
- Pablo Clement ([email protected])
Beta-testers
- Richard Blanc ([email protected])
- Solveig Gaydon Ohl ([email protected])
- Quentin Feuillade Montixi ([email protected])