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.
Acquired practical understanding of unsupervised learning, recommender systems, and reinforcement learning, including clustering (k-means), collaborative filtering, and Q-learning fundamentals.
Completed the “Generative AI: Introduction to Large Language Models” course on LinkedIn Learning, gaining a clear understanding of how LLMs work, their core components, practical…
