42_ml_modules

[42 curriculum] One week to learn basics in Machine Learning! 🤖 (well, as one of the authors, It was just to get the XP because the pedago of 42Paris was too slow to give it to me.). This project is a Machine Learning bootcamp created by 42 AI.

Even if my modules are made within more or less a week (most of the student will need far more times to learn the notions and code the functions/program), the solutions are done seriously and can inspired other students I hope.

Forewords:
The subjects were made by 42AI and are now a part of the 42 curriculum thanks to the hard work of authors.

Module 00: Stepping Into Machine Learning

Get started with some linear algebra and statistics

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

Module02 - 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.

Module02 - 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.

Module03 - 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.

Module04 - Regularization

Fight overfitting!

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