Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
