- Natural language processing
	- concerned with the interactions between computers and human languages
	- identify the structure and meaning of words, sentences and text
	- Applications
		- sentiment analysis
		- text summarization
		- named entity recognition
	- data can be structured or unstructured (posts/reviews or address and date formats)
	- Regular expressions
		- text strings that are used to find patterns in text
		- [Pythex](https://pythex.org) is very helpful for testing regex
		- regex can be used to prepare data for models
			- remove punctuation and replace with spaces
			- remove numbers 
			- remove any extra spaces
			- can also convert to lowercase if you are not trying to find named entities
				- if not trying to find named entities should convert all to lower case so ex. October = october
	- tokenization is converting a string to a list of words
		- each token is evaluated separately
		- some words are not helpful and should be removed ("a", "the", "in" etc.)
	- stemming
		- converting words into their most root version
		- running -> run
		- desperately -> desper
		- studies -> studi
		- words in sentence and roots have essentially the same meaning
	- lemmatization
		- ensures that stem version of the word is a real word
		- desperately -> desper -> desperate
- bag of words representation
	- tokenization -> map words to indexes -> count word occurrences per document.
	- first tokenize sentence
	- then build vocabulary overall documents in corpus by tokenizing them
	- each phrase transformed into vector based on each words frequency in that phrase
		- vector dimensions is size of vocabulary
		- creates sparse matrix
	- can use this to categorize phrases by meaning, compare similarity or sentiment
	- cosine similarity matrix
		- can be used to compare similarity of phrases
		- 1.0 means that it is the same phrase
- Term Frequency - Inverse Document Frequency
	- weights words based on importance across documents
	- high weight to word that appears often in one document but not in many documents in the corpus
		- avoids words like "the" and "in"
		- words like these are likely to be very descriptive of the contents of the document
	- $$tfidf(w,d) = tf * \log\left( \frac{N+1}{N_{w} + 1} \right) + 1$$
	- $N_{w}$: number of documents in training set that word $w$ appears in
	- $N$: number of documents in training set
	- $tf$ (term frequency): number of times that the word $w$ appears in the query document $d$
		- $tf(w, d) = \frac{{\text{number of w in d}}}{\text{total words in d}}$
	- importance of word in specific document in comparison to the entire corpus
- N-grams
	- splitting sentences into groups of n words instead of individual words when tokenizing
	- can help to analyze relationships between words
- Topic modeling
	- unsupervised learning to group them into topics
	- Latent Dirichlet Allocation (LDA) is a popular technique
	- Logistic regression, Naive Bayes and SVMs can be used for text classification
		- deep learning is best way to do this
- Word embeddings
	- vector representations of words
	- captures meaning, relationships to other words and context in a continuous vector space
	- ex. king - man + woman = queen
	- models can dynamically create vector embeddings based on context for words with different meanings
	-