Machine Learning Fundamentals (en)

The “Machine Learning Fundamentals” course provides a comprehensive overview of fundamental concepts and techniques in the field of machine learning. On the first day of the course, students will be introduced to the Jupyter Notebook environment, with an option to participate in an optional introductory session. Subsequently, key concepts such as model representation, cost functions, gradient descent, and vectorization will be explored. Through a series of practical lessons, students will gain skills in applying techniques such as multiple linear regression, learning rate optimization, and feature engineering using Python and the Scikit-Learn library.

On the second day, the course will delve deeper into topics such as logistic regression, neural networks, support vector machines, and decision trees. Through interactive labs and coding exercises, participants will implement these algorithms from scratch and gain insights into their practical applications. The course will also cover essential concepts in model evaluation, including cross-validation, bias-variance tradeoff, and hyperparameter tuning.

Category: Artificial Intelligence

Machine Learning