Developed a solid understanding of probability and statistics for machine learning and data science, focusing on data distributions, sampling, and inference.
Gained a solid foundation in supervised learning, covering linear regression, logistic regression, and key ML concepts like loss functions, gradient descent, and model evaluation.
Developed hands-on skills in building neural networks and decision trees, applying regularization, and optimizing model performance through tuning, feature scaling, and advanced techniques.
