Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised 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 3,393회
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 1번째 강좌:
초급 단계
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
완료하는 데 약 12시간 필요
영어
자막: 영어
배울 내용
Describe text classification and related terminology (e.g., supervised machine learning)
Apply text classification to marketing data through a peer-graded project
Apply text classification to a variety of popular marketing use cases via structured homeworks
Train, evaluate and improve the performance of the text classification models you create for your final project
귀하가 습득할 기술
- Assess Marketing Problems
- Supervised Learning Process
- Supervised Learning
- Classification Models
- Supervised Learning Outcomes
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 1번째 강좌:
초급 단계
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
완료하는 데 약 12시간 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 8시간 필요
The Supervised Machine Learning Workflow
In this module, we will learn about the different types of machine learning that exist and the operational steps of building a supervised machine learning model. We will also cover performance metrics of text classification.
완료하는 데 8시간 필요
4개 동영상 (총 61분), 3 개의 읽기 자료, 2 개의 테스트
완료하는 데 1시간 필요
Neural Networks and Deep Learning
In this module, we will learn about neural networks and supervised machine learning. Then we will dive into real supervised machine learning projects and the key decisions that need to be made when conducting one's own project.
완료하는 데 1시간 필요
2개 동영상 (총 39분), 2 개의 읽기 자료, 1 개의 테스트
완료하는 데 2시간 필요
Getting Started with Google Colab and Deep Learning
In this module, we will learn how to work in the Google Colab and Google Drive environment. We will get started with supervised learning by using a wrapper for Google’s Tensorflow and transformer models.
완료하는 데 2시간 필요
2개 동영상 (총 67분), 2 개의 읽기 자료, 1 개의 테스트
완료하는 데 1시간 필요
Linear Models and Classification Metrics
In this module, we will learn how to workshop a variety of supervised machine learning models that rely on linear-based models. We will also learn how to perform an external performance analysis of models in sci-kit learn.
완료하는 데 1시간 필요
2개 동영상 (총 17분), 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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