1. Image processing and classification of images to classify images of areas 2.images taken from satellite into categories, like park, open area, road, forest, pond or roof top 3. Building robust forecasting models for complex time series data, with several time series correlated with each other 4. Train word embedding models for natural language processing applications in Python using Gensim. Train word2vec word embedding model on text data, visualize a trained word embedding model using Principal Component Analysis and load pre-trained word2vec and GloVe word embedding models from Googleand Stanford. Develop models for natural language understanding 4. Using libopenCV, prepare models to identify and track intrusive objects in an open field, under video surveillance 5. Personalization and recommendation engines for certain classes of objects.