This project is a Python programming and Machine Learning bootcamp created by 42 AI.
No prior Python programming or Machine Learning experience is required! Your mission, should you choose to accept it, is to come and learn some of the essential knowledge for Machine Learning, Data Science and statistics, in a single week. You will start with the basics of the Python language and then get acquainted with some libraries that are invaluable to any programmer interested in the field of AI or data science.
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.
Let's get started with the Python language! 🐍
Basic setup, variables, types, functions, ...
Get acquainted with object-oriented programming and much more.
Objects, cast, inheritance, built-in functions, generator, construtors, iterator, ...
Continue practicing with more advanced Python programming exercises.
Decorators, multiprocessing, lambda, build package, ...
Learn how to use the NumPy library, manipulate multidimensional arrays and perform complex mathematical operations on matrices!
NumPy array, slicing, stacking, dimensions, broadcasting, normalization, etc...
Time to use a Python library that will allow you to manipulate dataframes.
Pandas! And Bamboos! 🐼
Get started with some linear algebra and statistics
Sum, mean, variance, standard deviation, vectors and matrices operations.
Hypothesis, model, regression, cost function.
Implement a method to improve your model's performance: gradient descent, and discover the notion of normalization
Gradient descent, linear regression, normalization.
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.
Discover your first classification algorithm: logistic regression!
Logistic hypothesis, logistic gradient descent, logistic regression, multiclass classification.
Accuracy, precision, recall, F1-score, confusion matrix.
Fight overfitting!
Regularization, overfitting. Regularized cost function, regularized gradient descent.
Regularized linear regression. Regularized logistic regression.