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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Bootcamp to learn the basics for Machine Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 253 |
| 2026-08-05 | 253 |
| 2026-08-06 | 253 |
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<p align="center"> <img src="assets/42ai_logo.png" width="200" alt="42 AI Logo" /> </p> <h1 align="center"> Bootcamp Machine Learning </h1> <h3 align="center"> One week to learn the basics in Machine Learning! :robot: </h3> <br/> ### Table of Contents - [Download](#download) - [Curriculum](#curriculum) - [Module05 - Stepping Into Machine Learning](#module05---stepping-into-machine-learning) - [Module06 - Univariate Linear Regression](#module06---univariate-linear-regression) - [Module07 - Multivariate Linear Regression](#module07---multivariate-linear-regression) - [Module08 - Logistic Regression](#module08---logistic-regression) - [Module09 - Regularization](#module09---regularization) - [Acknowledgements](#acknowledgements) - [Contributors](#contributors) - [Beta-testers](#beta-testers) <br/> This project is a Machine Learning bootcamp created by [42 AI](http://www.42ai.fr). As notions seen during this bootcamp can be complex, we very strongly advise students to have previously done the following bootcamp: - [Python](https://github.com/42-AI/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. <br/> ## Download The pdf files of each module can be downloaded from our realease page: [https://github.com/42-AI/bootcamp_machine-learning/releases](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. <br/> ## 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]) - Amir Mahla ([email protected]) - Mathieu Perez ([email protected]) ### Beta-testers - Richard Blanc ([email protected]) - Solveig Gaydon Ohl ([email protected]) - Quentin Feuillade--Montixi ([email protected])
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matched fp:93ca20044da26456, topic:learning