Neural Machine Translation (NMT) has achieved significant progress especially for languages with large amounts of data (referred to as high resource languages). However, most of the world languages lack sufficient data and are thus considered as low resource or endangered. Previous research explored various techniques for improving NMT performance on low resource languages, with no guarantees that they will perform similarly on other languages. In this work, we explore various low resource NMT techniques for improving performance on Moroccan Arabic (Darija), a dialect of Arabic that is considered a low resource language. We experiment with three techniques that are prominent in low resource Natural Language Processing (NLP), namely: back-translation, paraphrasing and transfer learning. Our results indicate that transfer learning, especially in combination with back-translation is effective at improving translation performance on Moroccan Arabic, achieving a BLEU score of 26.79 on Darija to English and 9.98 on English to Darija.