This is my implementation for 42AI's bootcamp
day00 to day04: Bootcamp Python
- Basics (variables, functions, generator, construtors, iterator, decorators)
- Introduction to Numpy (NumPy array, slicing, stacking, dimensions, broadcasting, normalization)
- Introduction to Pandas (Pandas DataFrame)
day05 to day09: Bootcamp Machine Learning (Python)
- Get started with some linear algebra and statistics
Sum, mean, variance, standard deviation, vectors and matrices operations. Hypothesis, model, regression, cost function.
- Univariate Linear Regression
Gradient descent, linear regression, normalization.
- Multivariate Linear Regression
Multivariate linear hypothesis, multivariate linear gradient descent, polynomial models. Training and test sets, overfitting.
- Logistic Regression
Logistic hypothesis, logistic gradient descent, logistic regression, multiclass classification. Accuracy, precision, recall, F1-score, confusion matrix.
- Regularization
Regularization, overfitting. Regularized cost function, regularized gradient descent. Regularized linear regression. Regularized logistic regression.