Data Preprocessing: Ability to clean, preprocess, and transform raw data for analysis.
Supervised Learning Mastery: Proficiency in algorithms like linear regression, decision trees, and support vector machines.
Unsupervised Learning: Skills to implement clustering and dimensionality reduction techniques.
Model Evaluation: Expertise in using metrics to assess model performance and refine models.
Advanced Techniques: Hands-on experience with ensemble methods, hyperparameter tuning, and deep learning integration.