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21_ds_branch

The piscine focuses on basic programming skills with Python and the most popular and useful data science libraries. The participants will be able to collect data with parsing, preprocess data using Pandas and SQL and build pipelines with machine learning algorithms.

42ai-python-bootcamp

car-model-classification

Car Model classification using Stanford Cars Dataset for Grab AI For Sea challenge on computer vision (https://www.aiforsea.com/computer-vision)

carnd-extended-kalman-filter-project

Self-Driving Car Nanodegree Program Starter Code for the Extended Kalman Filter Project

ccxt

A JavaScript / Python / PHP cryptocurrency trading library with support for more than 90 bitcoin/altcoin exchanges

check-ocr

churn

This project is an introduction to artificial neural nets: fully-connected neural nets, hidden layers, activation functions, back-propagation, dropout.

computorv1

This project aims to make you code a simple polynomial equation solver.

docs

TensorFlow documentation

dslr

This project is about data exploration and logistic regression.

finrl_with_fundamental_data

Trained an automated stock trading model with companies' fundamental data using FinRL, a Deep Reinforcement Learning library developed by AI4Finance.

ft_linear_regression

This project is an introduction to machine learning.

handwritten-digit-recognition

Handwritten digit recognition with the MNIST dataset.

images_example

A simple example program made for the 42 students going into the graphic branch. Its purpose is to help them understanding the way images are working in the MLX (the graphcal lib we use).

llmspracticalguide

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

medaymenf

ml-module-00

The goal of this module is to discover the concept of linear regression. You will study, in this module the keys concepts in linear algebra to achieve univariate linear regression.

ml-module-01

The goal of this module is to get started with the basics of linear regression. You will study, in the field of machine learning, what we call an hypothesis, cost function, gradient descent and some notions of feature scaling.

ml-module-02

The goal of this module is building on what you did on the previous modules. You will extend the linear regression to handle more than one features. Then you will see how to build polynomial models and how to detect overfitting.

ml-module-03

The goal of this module is to discover your first classification algorithm: logistic regression. You will learn its loss function, gradient descent and some metrics to evaluate its performance.

ml-module-04

The goal of this module is to discover the concepts of regularization and how to implement it into the algortihms you already saw until now.

multilayer-perceptron

This project is an introduction to artificial neural networks thanks to the implementation of a multilayer perceptron.

myspotify

This project is an introduction to algorithms used for recommendations: non-personalized, content-based, collaborative filtering.

notebooks

Repository for deepdoctection tutorial notebooks

openaedmap-frontend

Map which shows locations of defibrillators (AEDs) and allows adding new ones.

python-module-00

This first module of Python is designed to to get started with the Python language. You will study basic setup, variables, data types, functions, ...

python-module-01

The goal of this module is to get started with the Python language. You will study objects, classes, inheritance, built-in functions, magic methods, generator ...

python-module-02

The goal of this module is to tackle advanced notions of Python. You will learn more about decorators, lambda, context manager, build package,

python-module-03

This fourth module of Python is designed to to get started with the library Numpy.

python-module-04

This fifth module is dedicated to the manipulation of Pandas library, widely used in datascience field.