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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Text Marketing Analytics
von
Spezialisierung Text Marketing AnalyticsUniversity of Colorado BoulderÜber diesen Kurs
3.341 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 1 von 3 in
Stufe „Anfänger“
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
Ca. 12 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Was Sie lernen werden
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
Kompetenzen, die Sie erwerben
- Assess Marketing Problems
- Supervised Learning Process
- Supervised Learning
- Classification Models
- Supervised Learning Outcomes
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 1 von 3 in
Stufe „Anfänger“
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
Ca. 12 Stunden zum Abschließen
Englisch
Untertitel: Englisch
von
University of Colorado Boulder
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.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Dieses Kurs ist Teil des reinen Onlineabschlusses Master of Science in Data Science von University of Colorado Boulder.
Wenn Sie in das komplette Programm aufgenommen werden, werden Ihre Kurse auf Ihren Abschluss angerechnet.
Lehrplan - Was Sie in diesem Kurs lernen werden
8 Stunden zum Abschließen
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.
8 Stunden zum Abschließen
4 Videos (Gesamt 61 min), 3 Lektüren, 2 Quiz
1 Stunde zum Abschließen
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.
1 Stunde zum Abschließen
2 Videos (Gesamt 39 min), 2 Lektüren, 1 Quiz
2 Stunden zum Abschließen
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.
2 Stunden zum Abschließen
2 Videos (Gesamt 67 min), 2 Lektüren, 1 Quiz
1 Stunde zum Abschließen
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.
1 Stunde zum Abschließen
2 Videos (Gesamt 17 min), 2 Lektüren, 1 Quiz
Über den Spezialisierung Text Marketing Analytics
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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