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.
Ce cours fait partie de la Spécialisation Text Marketing Analytics
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Spécialisation Text Marketing AnalyticsUniversité du Colorado à BoulderÀ propos de ce cours
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Dates limites flexibles
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Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 3 dans le
Niveau débutant
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
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
Compétences que vous acquerrez
- Assess Marketing Problems
- Supervised Learning Process
- Supervised Learning
- Classification Models
- Supervised Learning Outcomes
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 3 dans le
Niveau débutant
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université du Colorado à 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.
Commencez à travailler pour obtenir votre master
Ce cours fait partie du diplôme intégralement en ligne Master of Science in Data Science de Université du Colorado à Boulder.
Si vous êtes admis(e) au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
Programme de cours : ce que vous apprendrez dans ce cours
8 heures pour terminer
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 heures pour terminer
4 vidéos (Total 61 min), 3 lectures, 2 quiz
1 heure pour terminer
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 heure pour terminer
2 vidéos (Total 39 min), 2 lectures, 1 quiz
2 heures pour terminer
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 heures pour terminer
2 vidéos (Total 67 min), 2 lectures, 1 quiz
1 heure pour terminer
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 heure pour terminer
2 vidéos (Total 17 min), 2 lectures, 1 quiz
À propos du Spécialisation 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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