Welcome to my NLP class notes

Welcome to my NLP class notes#

These class notes are for use with Jupyter notebooks in the NLP course. NLP is a trending field and requires a range of skills, most of which we will touch upon in the material for this course. See NLP skills that employers are looking for in 2022. Gen AI skills data scientists need in 2024.

OVERVIEW#

Natural Language Processing (NLP) and Gen AI have found several applications in business. There is now a foundation of content that students who wish to work in this field need to know and this course is aimed at providing students with a conceptual understanding of the field and its business applications, and a technical toolkit to implement NLP models. The course will include using AWS SageMaker and Bedrock, cloud services for AI/machine learning. Free credits are made available on AWS Educate.

LEARNING OBJECTIVES#

  • Explain the business areas in which NLP may be applied.

  • Describe the important concepts and mathematical models for NLP.

  • Implement programming languages and toolkits on NLP models for business applications.

  • Learn how to build and deploy NLP models on cloud infrastructure.

PREREQUISITES#

Students will need an understanding of Linear Algebra (familiarity with matrix multiplication, inverses, determinants, eigensystems, etc.) plus some calculus-based probability, and multivariate calculus. Students should also be able to program in Python and use IDEs such as Jupyter and VSCode.

RELATED CONTENT#

While these notes are more or less self-contained, and link to many resources, you can also lean on these resources:

  1. For some basics in text handling and regex, read Ch7&8 in Automate the Boring Stuff with Python.

  2. Basics of NLP are in the Natural Language Tool Kit (NLTK).

  3. Data for all course notebooks.

  4. Images for all course notebooks.

Note that wherever code has been used from public sources, it has been liberally cited, though some code is generated using large language models and it is hard to trace the origin of such code. These notes are solely for pedagogical purposes and to aid my students, and please feel free to email me comments, references, etc. Feedback is always welcome.

LICENSE#

MIT License

Copyright (c) 2021-2024 Sanjiv R. Das

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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