This Scikit-learn Test evaluates candidates’ ability to build, train, and optimize machine learning models using the scikit-learn library. It assesses knowledge of supervised and unsupervised learning, data preprocessing, feature engineering, and model evaluation techniques for real-world applications.
This comprehensive Matplotlib Test evaluates candidates’ ability to create, customize, and interpret data visualizations using Python’s Matplotlib library. It assesses practical skills in plotting, styling, data representation, and graphical storytelling to ensure candidates can present complex datasets clearly and effectively.
The Artificial Neural Network Test evaluates candidates’ understanding of neural network concepts, deep learning architectures, activation functions, optimization techniques, backpropagation, and practical AI applications.
The Reinforcement Learning Test evaluates candidates’ expertise in reinforcement learning fundamentals, reward optimization, policy learning, Q-learning, Deep Q Networks (DQN), and decision-making models.
This comprehensive R Coding Test evaluates candidates on R fundamentals, data wrangling, vectorization, statistical operations, model building, and visualization techniques.