Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.
제공자:
Text Marketing Analytics 특화 과정콜로라도 대학교 볼더 캠퍼스이 강좌에 대하여
최근 조회 2,962회
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 강좌:
중급 단계
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
완료하는 데 약 13시간 필요
영어
자막: 영어
배울 내용
Describe the concept of topic modeling and related terminology (e.g., unsupervised machine learning)
Apply topic modeling to marketing data via a peer-graded project
Apply topic modeling to a variety of popular marketing use cases via homework assignments
Evaluate, tune and improve the performance the topic model you create for your project
귀하가 습득할 기술
- Topic Model
- Machine Learning
- Python Programming
- Unsupervised Text Classification
- Data Structure
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 강좌:
중급 단계
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
완료하는 데 약 13시간 필요
영어
자막: 영어
제공자:
콜로라도 대학교 볼더 캠퍼스
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
석사 학위 취득 시작
이 강좌은(는) 콜로라도 대학교 볼더 캠퍼스의 100% 온라인 Master of Science in Data Science 중 일부입니다.
전체 프로그램을 수료하면 귀하의 강좌가 학위 취득에 반영됩니다.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 4시간 필요
What is topic modeling?
In this module, we will cover the fundamental concepts of topic modeling, also known as unsupervised machine learning on unstructured text documents. We will contrast unsupervised methods to supervised ones and survey common applications of topic modeling.
완료하는 데 4시간 필요
1개 동영상 (총 20분), 3 개의 읽기 자료, 1 개의 테스트
완료하는 데 4시간 필요
The Assumptions of a Topic Model, Bag of Words, and Natural Language Processing
In this module, we will go under the hood inside a topic modeling approach and understand what assumptions drive topic model fit. We will also uncover how bag-of-words approaches to topic modeling work, and the natural language processing required to produce meaningful topic modeling features.
완료하는 데 4시간 필요
2개 동영상 (총 24분), 1 개의 읽기 자료, 2 개의 테스트
완료하는 데 1시간 필요
Prepping Amazon Review Data
In this module, we will cover how to parse through JSON-like data and segment it to create a corpus that is ready for the topic modeling process. We will cover how the data for your project is structured and its taxonomy.
완료하는 데 1시간 필요
2개 동영상 (총 27분), 2 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
Pre-Processing Text and Training a Topic Model
In this module, we will take Amazon review data and load it into a corpus to preprocess it. We will cover how to build topic models from the data and also save those topic models.
완료하는 데 2시간 필요
2개 동영상 (총 29분), 2 개의 읽기 자료, 1 개의 테스트
Text Marketing Analytics 특화 과정 정보
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also often involve relational and connected and involve networks. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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