Accurate prediction of wind speed is crucial for local weather forecasting services (e.g., dealing with wind power industry and the Olympic Winter Game). Based on three machine learning algorithms (LASSO regression, random forest and deep learning), this paper demonstrates three models for adjusting the 10 m wind speed in North China predicted by the numerical weather forecast model of ECMWF. Firstly, the LASSO regression algorithm is applied to identify the features which significantly affect the nearsurface wind speed, among all the available meteorological elements. The extracted feature set is used as input for each machine learning algorithm to establish a model to adjust the ECMWFpredicted wind speed. Feature extraction helps to reduce the amount of computation, storage overhead and the complexity of the model, hence to facilitate the generalization of the model. The results of the three machine learning algorithms are compared with that of the traditional MOS method. All the three machine learning methods show a better performance in adjusting the wind speed than that of MOS, indicating great potential of the machine learning methods in improving local weather forecast.