Incoming Junior Interested in ML Internships — What Should I Focus on Next? [R](reddit.com)

Hi everyone, I'm a Computer Science + Applied Mathematics major at a T15 CS university, focusing on machine learning, and I'll be an incoming junior this fall. My long-term goal is to land ML-focused internships and eventually work in machine learning or AI-related roles. I recently completed an introductory AI/ML course that covered the fundamentals of: * Data preprocessing (handling missing data, feature scaling, train/test splits, categorical encoding) * Regression (linear, polynomial, SVR, decision trees, random forests) * Classification (logistic regression, KNN, SVMs, Naive Bayes, decision trees, random forests) * Clustering (K-Means, hierarchical clustering) * Association rule learning (Apriori, Eclat) * Reinforcement learning (UCB, Thompson Sampling) * NLP (tokenization, bag-of-words, sentiment analysis) * Deep learning (ANNs, CNNs) * Dimensionality reduction (PCA, LDA, Kernel PCA) * Model selection and boosting (cross-validation, grid search, XGBoost) I've also completed two research/internship experiences: * Built a Human Activity Recognition model using KNN. * Developed a Louvain clustering pipeline for beauty product datasets. From a coursework perspective, I've completed Linear Algebra, Calculus III, and will soon be taking Applied Linear Algebra and Probability & Statistics. Given my current background, what projects, activities, courses, competitions, or skills would you recommend I focus on over the next year to become a stronger candidate for ML internships? Are there any gaps in my knowledge that stand out? submitted by /u/Reasonable_File663 [link] [Kommentare]