snehac-miner/nlp_practicum

Machine Learning Guild - NLP Practicum

Textbook:

• Speech and Language Processing (SLP):
• SLP YouTube Videos:

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
• Supplementary Material: watch lesson_databases videos

2. Text Preprocessing

• Topics: POS tagging, dependency parsing, rule-based matching, phrase dectection
• Technology: SpaCy, gensim
• Prework: Read section 2.1-2.4 SLP and/or 2.1-2.5 SLP videos , section 8.1-8.3 SLP, and chapter 5 Collocations
• Supplementary Material: watch lesson_automation videos

3. 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_object_oriented_python

4. Dimensionality Reduction

• Topics: PCA, latent semantic indexing (LSI), latent dirichlet allocation(LDA), topic coherence metrics
• Technology: scikit-learn, gensim
• Prework: Read TamingTextwiththeSVD

5. Word Embeddings

• Topics: Word2Vec, GloVe, FastText
• Technology: scikit-learn, gensim
• Prework: Read section 6.8-6.13 SLP, Efficient Estimation of Word Representations in Vector Space & Distributed Representations of Words and Phrases and their Compositionality

6. Deep Learning for NLP 1

• Topics: Neural networks with word embeddings
• Technology: keras, gensim, FLAIR (pytorch)
• Prework: Read NLP (Almost) from Scratch and A Primer on Neural Network Models for Natural Language Processing

7. Deep Learning for NLP 2

• Topics: Neural networks with word embeddings, Contextual Word Embeddings
• Technology: keras, gensim, FLAIR (pytorch)
• Prework: Read NLP (Almost) from Scratch and A Primer on Neural Network Models for Natural Language Processing

8. 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

SUPPLEMENTARY MATERIAL

Automation

• 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

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

Object Oriented Python

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