NLP has come a long way from the early rule-based systems to the modern neural networks. It provides guidelines to practitioners for choosing models that can accomplish different NLP tasks and points out the strengths and deficiencies of various approaches. Following this, the paper traces back the recent rise of machine learning and deep learning techniques and discusses the possibilities in handling the complexity of human language and fitting new data.