Alexjmsherman/nlp_practicum_health

Machine Learning Guild - NLP Practicum

Textbook:

• Speech and Language Processing (SLP): https://web.stanford.edu/~jurafsky/slp3/
• SLP YouTube Videos: https://www.youtube.com/playlist?list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm

LESSONS

0. Configuration (Pre-work)

• Topics: course overview, git bash, python config.ini files, conda virtual environments
• Technology: git bash, configparser, conda
• Homework: use the command line to search data among 1000's of server configuration files

1. Text Extraction

• Topics: Extract text from docx, pdf, and image files
• Technology: docx, PyPDF2, pdfminer.six, subprocess, pytesseract
• Homework: structure the annual reports into sections

2. Text Preprocessing

• Topics: lemmatization, POS tagging, dependency parsing, rule-based matching
• Technology: SpaCy
• Prework: Read section 2.1-2.4 SLP and/or 2.1-2.5 SLP videos
• Supplementary Material: watch lesson_2a_automation videos

3. Phrase (collocation) Detection

• Topics: acronyms, POS phrases, phrase dectection
• Technology: SpaCy, gensim
• Prework: Read section 8.1-8.3 SLP and chapter 5 Collocations (https://nlp.stanford.edu/fsnlp/promo/colloc.pdf)
• Supplementary Material: watch lesson_3a_databases videos

4. Text Vectorization (count-based methods)

• Topics: vector space model, TFIDF, BM25, Co-occurance matrix
• Technology: scikit-learn
• Prework: Read section 6.1-6.6 SLP
• Supplementary Material: watch lesson 4a_object_oriented_python

5. Dimensionality Reduction

• Topics: PCA, latent semantic indexing (LSI), latent dirichlet allocation(LDA), topic coherence metrics
• Technology: scikit-learn, gensim
• Prework: Read TamingTextwiththeSVD (ftp://ftp.sas.com/techsup/download/EMiner/TamingTextwiththeSVD.pdf)

6. Word Embeddings

• Topics: Word2Vec, GloVe, FastText, ELMO, ULMFit
• Technology: scikit-learn, gensim
• Prework: Read section 6.8-6.13 SLP, Efficient Estimation of Word Representations in Vector Space (https://arxiv.org/pdf/1301.3781.pdf), & Distributed Representations of Words and Phrases and their Compositionality (https://arxiv.org/pdf/1310.4546.pdf)

7. Text Similarity

• Topics: cosine similarity, distance metrics, l1 and l2 norm, recommendation engines
• Technology: scikit-learn, SpaCy, gensim
• Prework: Read section 2.5 SLP and/or 2.1-2.5 SLP videos

8. Document Classification

• Topics: document classification, evaluation metrics, machine learning experiment set-up
• Technology: scikit-learn
• Prework: Read chapter 4 & 5 SLP
• Supplementary Material: watch lesson 8a_pipeline_and_custom_transformers

SUPPLEMENTARY MATERIAL

2a. Automation (Optional)

• Topics: automate the process to collect data from https://www.annualreports.com
• Technology: requests, Jupyter Notebooks, BeautifulSoup, Scrapy
• Homework: automate the process to identify and download company 10-K annual reports

3a. Databases

• Topics: use sqlalchemy to create and populate a database, locally and on AWS
• Technology: sqlalchemy, sqllite, AWS RDS (MySQL)
• Homework: create and populate a database with sqlalchemy

4a. Object Oriented Python

• Topics: reconstruct scikit-learn's CountVectorizer codebase
• Technology: scikit-learn, object oriented Python

8a. Pipelines and Custom Transformers

• Topics: capture, format, and send logging messages to a variety of output. Exception Handling. Create an executable of a python package for deployment
• Technology: scikit-learn, logging, python exceptions, pyinstaller, argparse