Incoming Junior Interested in ML Internships — What Should I Focus on Next? [R]

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?