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Text Mining and Analytics for Machine Learning

An elective course in the Machine and Deep Learning Specialized Studies Certificate Program.

Course Description

With drastic improvement in computational and algorithmic capabilities over the past decade, and with successful application of rather simple sentiment analytics, the industry is moving on to gaining insight about human emotions and feelings of human beings from natural languages. This has resulted in Natural Language Processing to move from laboratories to the industry with such applications as text classification, language modeling, speech recognition, caption generation, document summarization, and chatbots. Today a large number of organization use Natural Language Processing to process textual data from social media to make decisions in messaging, selling, and in social entrepreneurship. This course provides a solid foundation in Text Mining and In Natural Language Processing. The course starts with an introduction to text mining using Python. Students will learn searching, reading, scrapping, cleaning, and processing text from multiple sources. The course will use Regular Expression, TextBlob, Word Vectors, and NLTK (Natural Language Toolkit). Students will also learn how to implement Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation respectively for text indexing and for topic modeling. Finally, students will learn Natural Language Understanding and its applications. Prerequisites: I&C SCI X426.75 Tools and Techniques for Machine Learning OR I&C SCI X426.73 Intro to Machine Learning and AI. I&C SCI_X426.64 Introduction to Programming with Python; expected ability to program, read from and write to files, use nested loops, conditions, functions, etc. Python is the programming language in this course - basic experience with Python is helpful. This course also assumes that you are comfortable with statistics mathematics including concepts in random variable and probability.

Prerequisites: I&C SCI X426.75 Tools and Techniques for Machine Learning OR I&C SCI X426.73 Intro to Machine Learning and AI. Python is the programming language in this course - basic experience with Python is helpful. This course also assumes that you are comfortable with statistics mathematics including concepts in random variable and probability.

  • Details
  • $820
  • October 18, 2021 to December 05, 2021
  • Delivery Mode: Online
  • Reg#: 00378
  • ID/Units: I&C SCI X426.77  (2.00)
    ( Section 1 )
  • Quarter: FALL 2021

Instructor


Rashed Iqbal, Ph. D is a Program Manager, Data Solutions at Teledyne Technologies in California. He has two areas of interest and expertise: Data Science and Machine Learning, and Transitioning Traditional Organization to Agile and Lean Methods. He practices, consults, and teaches in these domains. . He is part-time Adjunct Professor in Economics Department at UCLA where he teaches graduate courses in Data Science. Rashed also teaches at UCLA Extension and at UCI Continuing Education. Rashed undertook multiple entrepreneurial ventures in these areas. His current area of research is Narrative Economics that studies impact of the popular narratives and stories on economic fluctuations. He is using NLP and Deep Learning to extract narratives in human communication. He believes narrative extraction will revolutionize process of human communication to which Narrative Economics is just one of the applications. Rashed has a Ph.D. in Systems Engineering with focus on Stochastic and Predictive Systems and holds current CSM, CSP, PMI-ACP, and PMP certifications. He is also a Senior Member of the IEEE.

Textbook Information

Textbooks for your course may be purchased from any vendor or bookseller of your choice.

Required Textbook(s):

APPLIED TEXT ANALYSIS WITH PYTHON: ENABLING LANGUAGE-AWARE DATA PROD
Book - ISBN: 9781491963043
Bergfort, Bilbro, Ojeda, 1 ed, O'Reilly Media

Meeting Schedule

EventDateDayStart TimeEnd TimeLocationRoom
START10/18/2021Monday------Online (Access Begins)---
END12/05/2021Sunday------Online (Access Ends)---