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Chris J. Vargo
and
Scott Bradley
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.
Read more
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.
Read more
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.
This course 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/z6u4jn/supervised
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What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Hands-on labs in Python bolster theoretical instruction
Teaches skills that augments human capabilities in analyzing large-scale marketing data
Well-structured with high-quality materials
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Supervised Text Classification for Marketing Analytics with these
activities:
Complete the Python Refresher
Show steps
Start the course with a firm grasp of Python's capabilities.
Browse courses on
Python
Show steps
-
Review basic Python syntax, including data types, variables, and control structures.
-
Practice writing simple Python programs.
Practice Natural Language Processing (NLP) fundamentals
Show steps
Revise core NLP concepts like tokenization, stemming, and part-of-speech tagging to strengthen your foundation for the course.
Browse courses on
Natural Language Processing (NLP)
Show steps
-
Review introductory materials on NLP concepts
-
Complete online tutorials or exercises on tokenization, stemming, and part-of-speech tagging
Participate in a Study Group for Supervised Machine Learning
Show steps
Enhance your learning through collaboration and discussion with peers.
Browse courses on
Machine Learning
Show steps
-
Join or form a study group with other students taking the course.
-
Meet regularly to discuss the course material, work on assignments together, and quiz each other.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Review Supervised Machine Learning with Google Colab and Deep Learning
Show steps
Prepare to apply machine learning algorithms to real-world datasets and projects.
Browse courses on
Machine Learning
Show steps
-
Read the documentation for Google Colab and Deep Learning.
-
Follow along with the tutorials provided in the course.
-
Complete the practice exercises.
Complete the Tensorflow Tutorial
Show steps
Tensorflow is a key tool for this course. Following the official Tensorflow tutorial will enhance your understanding of its capabilities.
Browse courses on
TensorFlow
Show steps
-
Set up the Tensorflow environment
-
Work through the Tensorflow tutorials on basic operations, data types, and models
Practice Supervised Machine Learning Modeling
Show steps
Reinforce your understanding of supervised machine learning algorithms and gain proficiency in model development and evaluation.
Browse courses on
Machine Learning
Show steps
-
Use scikit-learn to implement various supervised machine learning algorithms, such as linear regression, logistic regression, and decision trees.
-
Practice feature engineering and data preprocessing techniques.
-
Evaluate the performance of your models using different metrics, such as accuracy, precision, and recall.
Solve text classification practice problems
Show steps
Reinforce your understanding of text classification by solving practice problems that simulate the challenges you'll face in the course.
Browse courses on
Text Classification
Show steps
-
Find online platforms or resources that provide text classification practice problems
-
Attempt to solve the problems using your knowledge of supervised machine learning and text processing techniques
-
Review your solutions and identify areas for improvement
Develop a Text Classification Model
Show steps
Apply your skills to a practical problem by building and evaluating a text classification model.
Browse courses on
Machine Learning
Show steps
-
Define the problem statement and collect a suitable dataset.
-
Choose and implement an appropriate machine learning algorithm.
-
Evaluate the performance of your model.
-
Write a report summarizing your findings.
Build a simple text classification model using a pre-trained BERT model
Show steps
Apply your knowledge of supervised machine learning and deep learning by building a text classification model using a pre-trained BERT model.
Browse courses on
BERT
Show steps
-
Choose a pre-trained BERT model and import it into your preferred programming environment
-
Prepare a text dataset for training and testing your model
-
Fine-tune the pre-trained BERT model on your dataset
-
Evaluate the performance of your model on a held-out test set
Mentor a Beginner in Supervised Machine Learning
Show steps
Solidify your knowledge by teaching others and reinforce your understanding of the concepts.
Browse courses on
Machine Learning
Show steps
-
Identify a mentee who is interested in learning about supervised machine learning.
-
Create a study plan and schedule regular meetings.
-
Guide your mentee through the key concepts and provide them with resources and support.
Contribute to an Open-Source Machine Learning Library
Show steps
Gain practical experience and make a valuable contribution to the machine learning community.
Browse courses on
Machine Learning
Show steps
-
Identify an open-source machine learning library that aligns with your interests.
-
Review the library's documentation and codebase.
-
Identify an area where you can make a contribution, such as fixing a bug or adding a new feature.
-
Submit a pull request with your changes.
Complete the Python Refresher
Show steps
Start the course with a firm grasp of Python's capabilities.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic Python syntax, including data types, variables, and control structures.
- Practice writing simple Python programs.
Practice Natural Language Processing (NLP) fundamentals
Show steps
Revise core NLP concepts like tokenization, stemming, and part-of-speech tagging to strengthen your foundation for the course.
Browse courses on
Natural Language Processing (NLP)
Show steps
Show steps
Show steps
- Review introductory materials on NLP concepts
- Complete online tutorials or exercises on tokenization, stemming, and part-of-speech tagging
Participate in a Study Group for Supervised Machine Learning
Show steps
Enhance your learning through collaboration and discussion with peers.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join or form a study group with other students taking the course.
- Meet regularly to discuss the course material, work on assignments together, and quiz each other.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Review Supervised Machine Learning with Google Colab and Deep Learning
Show steps
Prepare to apply machine learning algorithms to real-world datasets and projects.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read the documentation for Google Colab and Deep Learning.
- Follow along with the tutorials provided in the course.
- Complete the practice exercises.
Complete the Tensorflow Tutorial
Show steps
Tensorflow is a key tool for this course. Following the official Tensorflow tutorial will enhance your understanding of its capabilities.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Set up the Tensorflow environment
- Work through the Tensorflow tutorials on basic operations, data types, and models
Practice Supervised Machine Learning Modeling
Show steps
Reinforce your understanding of supervised machine learning algorithms and gain proficiency in model development and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Use scikit-learn to implement various supervised machine learning algorithms, such as linear regression, logistic regression, and decision trees.
- Practice feature engineering and data preprocessing techniques.
- Evaluate the performance of your models using different metrics, such as accuracy, precision, and recall.
Solve text classification practice problems
Show steps
Reinforce your understanding of text classification by solving practice problems that simulate the challenges you'll face in the course.
Browse courses on
Text Classification
Show steps
Show steps
Show steps
- Find online platforms or resources that provide text classification practice problems
- Attempt to solve the problems using your knowledge of supervised machine learning and text processing techniques
- Review your solutions and identify areas for improvement
Develop a Text Classification Model
Show steps
Apply your skills to a practical problem by building and evaluating a text classification model.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem statement and collect a suitable dataset.
- Choose and implement an appropriate machine learning algorithm.
- Evaluate the performance of your model.
- Write a report summarizing your findings.
Build a simple text classification model using a pre-trained BERT model
Show steps
Apply your knowledge of supervised machine learning and deep learning by building a text classification model using a pre-trained BERT model.
Browse courses on
BERT
Show steps
Show steps
Show steps
- Choose a pre-trained BERT model and import it into your preferred programming environment
- Prepare a text dataset for training and testing your model
- Fine-tune the pre-trained BERT model on your dataset
- Evaluate the performance of your model on a held-out test set
Mentor a Beginner in Supervised Machine Learning
Show steps
Solidify your knowledge by teaching others and reinforce your understanding of the concepts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a mentee who is interested in learning about supervised machine learning.
- Create a study plan and schedule regular meetings.
- Guide your mentee through the key concepts and provide them with resources and support.
Contribute to an Open-Source Machine Learning Library
Show steps
Gain practical experience and make a valuable contribution to the machine learning community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify an open-source machine learning library that aligns with your interests.
- Review the library's documentation and codebase.
- Identify an area where you can make a contribution, such as fixing a bug or adding a new feature.
- Submit a pull request with your changes.
Career center
Learners who complete Supervised Text Classification for Marketing Analytics will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists wield the power of programming, statistics, machine learning, and data analysis to build a bridge between raw data and actionable insights. These professionals can help companies make decisions based on trends in marketing data. In this course, you'll dive into text classification, a process which allows computers to classify and categorize this data in a way that is both efficient and meaningful.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, maintain, and evaluate machine learning models. An important part of this process is training models to understand how to classify and categorize the enormous amounts of data which can be gathered by a modern business. This course will be useful for building upon your foundation in this subject.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and interpreting data. These processes can all be applied to marketing data, and this course will introduce you to a key tool for this: supervised text classification. You'll get hands-on experience with the tools and techniques which drive this process.
Marketing Analyst
Marketing Analyst
Marketing Analysts determine how to best allocate their company's advertising budgets, discover the strength and weaknesses of different strategies, and develop plans for future marketing campaigns. This course will provide you with valuable experience in text classification, a skill essential for understanding customer sentiment and responding accordingly.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for planning, developing, and implementing marketing campaigns. This often requires them to analyze marketing data, which can involve text classification. This course will help you build a foundation in this concept and provide hands-on experience with supervised text classification techniques.
Financial Analyst
Financial Analyst
Financial Analysts consult management on decisions involving investments and help compile the statistical data necessary for preparing financial reports. This course will introduce you to text classification, a technique which can aid you in working with the large datasets that are common in this career.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use advanced analytical techniques to help organizations solve complex problems and make better decisions. This course will introduce you to text classification, a technique which can aid you in working with the large datasets that are common in this career.
Statistician
Statistician
Statisticians apply statistical techniques to a wide range of practical problems. This course will introduce you to text classification, a technique which can aid you in working with the large datasets that are common in this career.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts implement and optimize business intelligence (BI) systems, which make it possible for their companies to collect, manage, and analyze their data. This course will introduce you to text classification, a key skill for using BI systems.
Market Research Analyst
Market Research Analyst
Market Research Analysts gather and analyze market data, consult with clients on their marketing challenges, and help design and execute plans for marketing campaigns. This course will provide you with experience in text classification, a key skill for analyzing marketing data.
UX Designer
UX Designer
UX Designers research, design, and test user interfaces for websites, software, and other digital products. This course will teach you about how text classification can be used to improve user experience.
Technical Writer
Technical Writer
Technical Writers create documentation for software, hardware, and other technical products. This course will teach you about how text classification can be used to improve the quality of technical documentation.
Information Architect
Information Architect
Information Architects design and organize the structure and content of websites, intranets, and other digital products. This course will teach you about how text classification can be used to improve information architecture.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software systems and applications. This course will teach you about the role of text classification in the software development process.
Product Manager
Product Manager
Product Managers are responsible for the overall success of a product, including its design, development, and marketing. This course will help you build a foundation in the fundamentals of text classification, which can be useful in any product management role.
For more career information including salaries, visit:
OpenCourser.com/course/z6u4jn/supervised
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Supervised Text Classification for Marketing Analytics.
Provides a comprehensive overview of sentiment analysis and opinion mining. It covers a wide range of topics, including text preprocessing, feature extraction, and sentiment classification. It great resource for students and practitioners who want to learn more about sentiment analysis and opinion mining.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It great resource for students and practitioners who want to learn more about deep learning.
Provides a practical introduction to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It great resource for students and practitioners who want to learn more about deep learning.
Provides a comprehensive overview of natural language processing and text mining. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification. It great resource for students and practitioners who want to learn more about natural language processing and text mining.
Provides a comprehensive overview of natural language processing, covering topics such as tokenization, stemming, lemmatization, and parsing. It valuable resource for students and practitioners who want to learn more about the fundamentals of NLP.
Provides a comprehensive overview of information retrieval. It covers a wide range of topics, including text preprocessing, indexing, and ranking. It great resource for students and practitioners who want to learn more about information retrieval.
Save
Provides a comprehensive overview of data mining. It covers a wide range of topics, including data preprocessing, feature selection, and model evaluation. It great resource for students and practitioners who want to learn more about data mining.
Provides a practical introduction to text analytics with Python. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification. It great resource for students and practitioners who want to learn more about text analytics.
Provides a practical introduction to machine learning with R. It covers a wide range of topics, including data preprocessing, model selection, and model evaluation. It great resource for students and practitioners who want to learn more about machine learning.
Provides a practical introduction to data analysis with Python. It covers a wide range of topics, including data cleaning, data manipulation, and data visualization. It great resource for students and practitioners who want to learn more about data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/z6u4jn/supervised
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Level
Introductory
Via
Coursera
Institution
University of Colorado Boulder
Instructors
Chris J. Vargo
Scott Bradley
Language
English
This course belongs to a
collection
called
Text Marketing Analytics. It's comprised of three courses:
- Supervised Text Classification for Marketing Analytics (viewing)
- Network Analysis for Marketing Analytics
- Unsupervised Text Classification for Marketing Analytics
Good to know
Hands-on labs in Python bolster theoretical instruction
Teaches skills that augments human capabilities in analyzing large-scale marketing data
Well-structured with high-quality materials
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