Skills and Insights Gained

In the Machine Learning course, I developed a deep understanding of both the theoretical foundations and practical applications of machine learning algorithms. I explored key concepts in supervised and unsupervised learning, including essential algorithms like linear and logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks.

I also gained valuable insights into important aspects of model evaluation, such as cross-validation, precision, recall, and F1-score, which are crucial for optimizing and selecting the best machine learning models. Through hands-on programming exercises, I was able to implement and test these algorithms, reinforcing my theoretical knowledge with practical, real-world applications.

The course further exposed me to advanced topics like hyperparameter tuning, regularization techniques, and ensemble methods, which are essential for improving model performance and addressing overfitting. I now have a solid foundation in applying machine learning techniques to solve complex data-driven problems.

As part of the course, I worked on a group project focused on traffic sign classification, where we implemented machine learning techniques to automatically recognize and classify traffic signs from images. This project allowed me to apply my skills in a real-world setting, combining image processing and machine learning.

If you're interested in learning more about this project and the methods used, you can follow this link to explore further.