We present our machine learning efforts, which show great promise towards early predictions of solar flare events. First, we present a data pre-processing pipeline that is built to extract useful data from multiple sources -- Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and SDO/Atmospheric Imaging Assembly (AIA) -- to prepare inputs for machine learning algorithms. Second, we adopt deep learning algorithms to extract/select features from raw HMI and AIA data. Third, we train machine learning models that capture both the spatial and temporal information from HMI magnetogram data for strong/weak flare classification and for predictions of flare intensities. Fourth, we show that using the ML-derived features gives almost as good performance as using active region parameters provided in HMI data files, i.e. features manually constructed based on physical principles. Last, case studies show a significant increase in the prediction score around 20 hours before strong solar flare events, which implies that early precursors appear at least 20 hours prior to the peak of a flare event.