Humans are great at classifying noises. We can hear a chirp and surmise that it belongs to a bird, we can hear an abstract noise and classify it as speech with a particular meaning and definition. This relationship between humans and audio classification forms the basis of speech and human communication as a whole. Translating this incredible ability to computers on the other hand can be a difficult challenge, to say the least. Whilst we can naturally decompose signals, how do we teach computers to do this, and how do we show what parts of the signal matter and what parts of the signal are irrelevant or noisy?

Part 2 covered certain linguistic aspects, challenges in preserving semantics, understanding shallow parsing, Named Entity Recognition (NER) and introduction to language models. In this part, we seek to cover the Bag-of-Words Model and TF-IDF vectorization of text, simple feature extraction techniques that yield numeric representation of the data. A Bag-of-Words model (BoW), is a simple way of extracting features from text, representing documents in a corpus in numeric form as vectors. A bag-of-words is a vector representation of text that describes the occurrence of words in a document. It is called a'bag' of words, because any information about the order or contextual occurrence of words in the document is discarded.