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CS 67 W — Introduction to Natural Language Processing (NLP) with Python

Quarter: Summer
Course Format: Flex Online (About Formats)
Duration: 8 weeks
Date(s): Jun 20—Aug 12
Refund Deadline: Jun 23
Unit: 1
Tuition: $545
Instructor(s): Oleg Melnikov
Limit: 35
Class Recording Available: Yes
Status: Cancelled
 
Summer
Flex Online(About Formats)
Date(s)
Jun 20—Aug 12
8 weeks
Refund Date
Jun 23
1 Unit
Fees
$545
Instructor(s):
Oleg Melnikov
Limit
35
Recording
Yes
Cancelled
Most companies collect and process vast amounts of textual data, including communications with customers, vendors, and staff. Enterprises use natural language processing (NLP), an emerging field of machine learning, and Python, a trending programming language, to make sense of the massive volumes of text. NLP professionals are eagerly sought after in today’s hot tech job market. They help companies to summarize, visualize, and digitize billions of documents to detect growing problems and reveal lucrative products/sales patterns. This course offers a rich introduction to NLP using Python. We cover problems of text pre-processing, feature extraction, text classification, summarization, document clustering, sentiment analysis, and word vector representation. Students develop intuition and skills for determining the correct NLP tool for the given problem and corresponding evaluation metrics to gauge and communicate their results to nontechnical audiences. Weekly assignments reinforce NLP concepts with Python in a Google Colab notebook. The course ends with short video presentations on NLP projects to prepare students for real-world job interviews.

Students are expected to have some Python programming skills and a basic understanding of college-level math and matrix algebra. Those with stronger preparation and some familiarity with natural language processing will be able to sharpen and deepen their expertise in this course. Before enrolling, students are encouraged to complete a self-evaluation of preparedness, reached through the Preliminary Syllabus link for this course at continuingstudies.stanford.edu. A lower preparedness score suggests that more study time may be needed or that students may wish to select the grading option of Credit/No Credit or No Grade Requested.

OLEG MELNIKOV
Lecturer, Cornell, Johns Hopkins, University of Chicago, and Higher School of Economics University; Senior Director of Data Science, ShareThis

Oleg Melnikov has developed and taught courses in statistics, machine learning, natural language processing, deep learning, Python, R, web development with SQL/PHP, and quantitative finance. He received a PhD in statistics from Rice University.