This paper proposes hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the practicality of quantum learning with the scalability of neutral atoms. The algorithm uses single-qubit operations in the digital setting and global driving based on the Rydberg Hamiltonian in the analog setting. A comprehensive numerical study is conducted on both classical and quantum data, including handwritten digit classification and unsupervised quantum phase boundary learning. The results show that digital-analog learning is feasible in the near term, requires shorter circuit depths, and is more robust to realistic error models compared to purely digital learning. The study suggests that digital-analog learning offers a promising path for improved variational quantum learning experiments. The paper discusses the structure of variational quantum algorithms (VQAs), which are widely considered QML models. VQAs involve a quantum circuit and a classical computer interacting to solve machine learning tasks. The quantum circuit contains parameters that are optimized using a loss function derived from circuit measurements. The paper explores digital-analog learning circuits, which combine digital gates (single-qubit rotations) and analog gates (global time evolution under the Rydberg Hamiltonian). These circuits are shown to be effective for both classical and quantum learning tasks. The paper also discusses noise models and physical hyperparameters for digital-analog learning. It shows that the choice of hyperparameters can be guided by physical principles rather than variational methods. The study finds that the optimal hyperparameters for digital-analog learning are $ \Delta/\Omega = 0.8 $ and $ R_{b}/a = 0.87 $, which provide a good balance between accuracy and noise robustness. The paper presents results for two representative learning problems: handwritten digit classification and quantum phase boundary learning. For the digit classification task, the digital-analog model outperforms the digital model in accuracy and robustness to noise. For the quantum phase boundary learning task, the digital-analog model successfully learns the phase diagram of the XXZ Hamiltonian, demonstrating its effectiveness in learning complex quantum phases. The study concludes that digital-analog learning offers significant advantages over purely digital learning in terms of performance and robustness to noise. The results suggest that digital-analog learning is a promising approach for near-term quantum learning experiments.This paper proposes hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the practicality of quantum learning with the scalability of neutral atoms. The algorithm uses single-qubit operations in the digital setting and global driving based on the Rydberg Hamiltonian in the analog setting. A comprehensive numerical study is conducted on both classical and quantum data, including handwritten digit classification and unsupervised quantum phase boundary learning. The results show that digital-analog learning is feasible in the near term, requires shorter circuit depths, and is more robust to realistic error models compared to purely digital learning. The study suggests that digital-analog learning offers a promising path for improved variational quantum learning experiments. The paper discusses the structure of variational quantum algorithms (VQAs), which are widely considered QML models. VQAs involve a quantum circuit and a classical computer interacting to solve machine learning tasks. The quantum circuit contains parameters that are optimized using a loss function derived from circuit measurements. The paper explores digital-analog learning circuits, which combine digital gates (single-qubit rotations) and analog gates (global time evolution under the Rydberg Hamiltonian). These circuits are shown to be effective for both classical and quantum learning tasks. The paper also discusses noise models and physical hyperparameters for digital-analog learning. It shows that the choice of hyperparameters can be guided by physical principles rather than variational methods. The study finds that the optimal hyperparameters for digital-analog learning are $ \Delta/\Omega = 0.8 $ and $ R_{b}/a = 0.87 $, which provide a good balance between accuracy and noise robustness. The paper presents results for two representative learning problems: handwritten digit classification and quantum phase boundary learning. For the digit classification task, the digital-analog model outperforms the digital model in accuracy and robustness to noise. For the quantum phase boundary learning task, the digital-analog model successfully learns the phase diagram of the XXZ Hamiltonian, demonstrating its effectiveness in learning complex quantum phases. The study concludes that digital-analog learning offers significant advantages over purely digital learning in terms of performance and robustness to noise. The results suggest that digital-analog learning is a promising approach for near-term quantum learning experiments.