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Ambica Ghai
Welcome to the Unsupervised Learning and Its Applications in Marketing course! In this course, you will delve into the fascinating world of unsupervised machine learning and its relevance to the field of marketing. Unsupervised learning is a powerful approach that allows us to uncover hidden patterns and insights from vast amounts of historical data without the need for explicit labels or human intervention. Through hands-on exercises and real-world examples, you will learn how to leverage the Python programming language to apply unsupervised learning algorithms in marketing contexts.
Read more
Welcome to the Unsupervised Learning and Its Applications in Marketing course! In this course, you will delve into the fascinating world of unsupervised machine learning and its relevance to the field of marketing. Unsupervised learning is a powerful approach that allows us to uncover hidden patterns and insights from vast amounts of historical data without the need for explicit labels or human intervention. Through hands-on exercises and real-world examples, you will learn how to leverage the Python programming language to apply unsupervised learning algorithms in marketing contexts.
Read more
Welcome to the Unsupervised Learning and Its Applications in Marketing course! In this course, you will delve into the fascinating world of unsupervised machine learning and its relevance to the field of marketing. Unsupervised learning is a powerful approach that allows us to uncover hidden patterns and insights from vast amounts of historical data without the need for explicit labels or human intervention. Through hands-on exercises and real-world examples, you will learn how to leverage the Python programming language to apply unsupervised learning algorithms in marketing contexts.
Throughout the course, you will explore various unsupervised learning techniques, such as clustering, dimensionality reduction, and association rule mining. These techniques will enable you to identify customer segments, uncover meaningful relationships between variables, and gain valuable insights into consumer behavior. By mastering the applications of unsupervised learning in marketing, you will acquire the skills to extract actionable knowledge from data, make data-driven decisions, and unlock new opportunities for your marketing strategies.
So, get ready to embark on a journey of discovery and innovation as you explore the fascinating world of unsupervised learning and its transformative applications in marketing. Let's dive in and unlock the hidden potential of data-driven marketing together!
To succeed in this course, you should have a basic understanding of Python.
You will also need certain software requirements, including Anaconda navigator.
Register for this course and see more details by visiting:
OpenCourser.com/course/ix0j72/unsupervised
What's inside
Syllabus
Fundamentals of Unsupervised Learning
In this module, you will be introduced to the exciting field of unsupervised learning and its applications in marketing. You will learn about various unsupervised learning algorithms and their functionalities, including clustering, dimensionality reduction, and association rule mining. Through hands-on exercises and practical examples, you will understand how these techniques can be used to uncover hidden patterns, identify customer segments, and gain valuable insights from large and complex marketing datasets. By the end of this module, you will have the knowledge and skills to apply unsupervised learning algorithms to solve marketing challenges, optimize campaigns, and make data-driven decisions that drive business growth. Get ready to unlock the potential of unsupervised learning and revolutionize your marketing strategies.
Read more
Syllabus
Fundamentals of Unsupervised Learning
In this module, you will be introduced to the exciting field of unsupervised learning and its applications in marketing. You will learn about various unsupervised learning algorithms and their functionalities, including clustering, dimensionality reduction, and association rule mining. Through hands-on exercises and practical examples, you will understand how these techniques can be used to uncover hidden patterns, identify customer segments, and gain valuable insights from large and complex marketing datasets. By the end of this module, you will have the knowledge and skills to apply unsupervised learning algorithms to solve marketing challenges, optimize campaigns, and make data-driven decisions that drive business growth. Get ready to unlock the potential of unsupervised learning and revolutionize your marketing strategies.
Read more
Clustering and Its Types
This module provides a comprehensive introduction to clustering algorithms and their practical application using Python. You will gain a solid understanding of the fundamental concepts of clustering and explore different algorithms such as k-means, hierarchical clustering, and DBSCAN. Through hands-on exercises and coding examples, you will learn how to preprocess and transform data, select appropriate clustering algorithms based on data characteristics, and evaluate the performance of clustering models. Additionally, you will acquire the necessary skills to interpret and visualize clustering results, allowing you to gain valuable insights into patterns and structures within your data. By the end of this module, you will be equipped with the knowledge and practical experience to confidently apply clustering algorithms to real-world marketing datasets, enabling you to uncover meaningful clusters and make informed business decisions based on the extracted knowledge.
Weekly Summative Assessment: Fundamentals of Unsupervised Learning and Clustering
This assessment is a graded quiz based on the modules covered this week.
Data-Driven Customer Segmentation
In this module, you will dive into the fascinating world of customer segmentation and dimensionality reduction techniques. Customer segmentation allows you to divide your customer base into distinct groups based on shared characteristics, behaviors, or preferences. By understanding the unique needs and preferences of different customer segments, you can tailor your marketing strategies to effectively target and engage each segment. You will learn various clustering algorithms and techniques to perform customer segmentation using Python, enabling you to uncover meaningful insights about your customers and optimize your marketing efforts. Additionally, you will explore dimensionality reduction techniques, which are essential for dealing with high-dimensional data and extracting the most relevant features. Through hands-on exercises and real-world examples, you will gain practical skills in implementing customer segmentation and dimensionality reduction techniques to unlock valuable insights and drive marketing success.
Dimensionality Reduction
This module provides an opportunity to apply dimensionality reduction algorithms using Python. You will explore different types of dimensionality reduction algorithms, such as Principal Component Analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders. Through practical exercises and code implementations, you will gain hands-on experience in reducing the dimensionality of datasets, visualizing high-dimensional data in lower dimensions, and interpreting the results. Additionally, you will be introduced to anomaly detection techniques, which involve identifying rare or unusual data points that deviate from the norm. By the end of this module, you will have a solid understanding of dimensionality reduction algorithms and their application in real-world marketing scenarios, as well as the ability to detect anomalies effectively.
Weekly Summative Assessment: Data-Driven Customer Segmentation and Dimensionality Reduction
This assessment is a graded quiz based on the modules covered this week.
Anomaly Detection
In this module, you will delve into the practical aspects of anomaly detection by implementing various types of anomaly detection algorithms using Python. You will gain hands-on experience in applying algorithms such as statistical methods, clustering-based approaches, and machine learning-based techniques to detect anomalies in marketing data. Through step-by-step coding examples and guided exercises, you will learn how to preprocess data, select appropriate algorithms for different scenarios, tune parameters, and evaluate the performance of the models. By the end of this module, you will have a solid understanding of the implementation details of different anomaly detection algorithms and be equipped to apply them effectively in real-world marketing scenarios.
Autoencoders and Association Learning
Welcome to the module on Autoencoders and Association Learning! In this module, you will explore the fascinating field of autoencoders and its application in association learning, specifically in market basket analysis. In this module, you will learn how to apply autoencoders to extract meaningful features from data and use them to perform association learning using techniques such as the Apriori algorithm and FP-Growth algorithm. Through hands-on exercises and real-world examples, you will gain practical skills in implementing autoencoders and conducting association analysis to discover valuable insights from large-scale transactional data.
Weekly Summative Assessment: Anomaly Detection, Autoencoders, and Association Learning
Semi-Supervised Learning
In this module, you will delve into the world of semi-supervised learning. Semi-supervised learning is a powerful technique that combines the strengths of both supervised and unsupervised learning. It leverages a small amount of labeled data along with a large amount of unlabeled data to improve model performance. Through this module, you will gain an understanding of the concepts and principles behind semi-supervised learning. You will also learn how to implement semi-supervised learning algorithms using Python, enabling you to leverage the vast amounts of unlabeled data available in many real-world scenarios. By the end of this module, you will have the knowledge and skills to apply semi-supervised learning techniques in various domains, unlocking new opportunities for predictive modeling and data analysis.
Recommender systems Using RBM
In this module, you will delve into the fascinating world of recommender systems and explore the concept of Boltzmann machines, which are powerful generative unsupervised models. You will gain a solid understanding of how Boltzmann machines work and their applications in recommendation systems. Through hands-on exercises and practical examples in Python, you will learn how to implement collaborative filtering using Boltzmann machines to make personalized recommendations. Additionally, this module will also touch upon the promising areas of unsupervised learning and provide insights into the future possibilities and advancements in the field. By the end of this module, you will be equipped with the knowledge and skills to build effective recommender systems and have a broader understanding of the potential of unsupervised learning in various domains.
Weekly Summative Assessment: Semi-Supervised Learning and Recommender systems Using RBM
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Ambica Ghai, who is recognized for their work in unsupervised learning
Develops skills in association rule mining, dimensionality reduction, and clustering
Combines theory with hands-on exercises and real-world examples
Uses popular programming language Python for practical implementation
Requires basic understanding of Python as a prerequisite
Assumes learners have access to Anaconda navigator software
Save this course
Save Unsupervised Learning and Its Applications in Marketing to your list so you can find it easily later:
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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 Unsupervised Learning and Its Applications in Marketing with these
activities:
Review the clustering process
Show steps
Refreshes and reinforces one's understanding of the clustering process, facilitating better comprehension during the course.
Browse courses on
Clustering
Show steps
-
Recall the fundamental concepts of clustering
-
Revise different clustering algorithms, such as k-means and hierarchical clustering
-
Practice applying clustering techniques to sample datasets
Review Unsupervised Learning Essentials
Show steps
Strengthen your foundation in unsupervised learning concepts by reviewing a book on the subject.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Read chapters 1-3 to gain an overview of unsupervised learning
-
Summarize the key concepts and techniques discussed in each chapter
-
Complete the practice exercises provided in the book
Solve practice problems on dimensionality reduction
Show steps
Provides hands-on practice in applying dimensionality reduction techniques, enhancing comprehension and proficiency.
Browse courses on
Dimensionality Reduction
Show steps
-
Work through practice exercises involving Principal Component Analysis (PCA)
-
Implement dimensionality reduction algorithms using Python code
-
Analyze the results and evaluate the effectiveness of different dimensionality reduction techniques
Five other activities
Expand to see all activities and additional details
Show all eight activities
Clustering Practice Drills
Show steps
Practice clustering algorithms to improve your understanding and skills in this area.
Browse courses on
Clustering
Show steps
-
Solve practice problems on clustering techniques using Python
-
Implement k-means, hierarchical, and DBSCAN algorithms on sample datasets
-
Use visualization techniques to interpret clustering results
Dimensionality Reduction with t-SNE
Show steps
Explore dimensionality reduction techniques by following a guided tutorial on t-SNE.
Browse courses on
Dimensionality Reduction
Show steps
-
Follow a step-by-step guide on implementing t-SNE in Python
-
Apply t-SNE to a high-dimensional dataset and visualize the results
-
Compare t-SNE with PCA for dimensionality reduction
Develop a case study on anomaly detection
Show steps
Enhances practical understanding and critical thinking skills by applying anomaly detection techniques to real-world scenarios.
Browse courses on
Anomaly Detection
Show steps
-
Identify a suitable dataset with anomalies
-
Select and implement appropriate anomaly detection algorithms
-
Analyze the results and interpret the detected anomalies
-
Summarize the findings and present them as a case study
Marketing Segmentation Project
Show steps
Apply unsupervised learning techniques to segment customers and develop a targeted marketing strategy.
Browse courses on
Customer Segmentation
Show steps
-
Collect and preprocess customer data
-
Cluster customers using appropriate algorithms
-
Analyze clustering results to identify customer segments
-
Develop targeted marketing strategies for each customer segment
Kaggle Anomaly Detection Competition
Show steps
Participate in a Kaggle competition to test your skills in anomaly detection and gain practical experience.
Browse courses on
Anomaly Detection
Show steps
-
Explore the competition dataset and familiarize yourself with the task
-
Research and implement various anomaly detection algorithms
-
Tune your models and submit your predictions for evaluation
-
Analyze the results and learn from the feedback provided by the competition organizers
Review the clustering process
Show steps
Refreshes and reinforces one's understanding of the clustering process, facilitating better comprehension during the course.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Recall the fundamental concepts of clustering
- Revise different clustering algorithms, such as k-means and hierarchical clustering
- Practice applying clustering techniques to sample datasets
Review Unsupervised Learning Essentials
Show steps
Strengthen your foundation in unsupervised learning concepts by reviewing a book on the subject.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to gain an overview of unsupervised learning
- Summarize the key concepts and techniques discussed in each chapter
- Complete the practice exercises provided in the book
Solve practice problems on dimensionality reduction
Show steps
Provides hands-on practice in applying dimensionality reduction techniques, enhancing comprehension and proficiency.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Work through practice exercises involving Principal Component Analysis (PCA)
- Implement dimensionality reduction algorithms using Python code
- Analyze the results and evaluate the effectiveness of different dimensionality reduction techniques
Five other activities
Expand to see all activities and additional details
Show all eight activities
Clustering Practice Drills
Show steps
Practice clustering algorithms to improve your understanding and skills in this area.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Solve practice problems on clustering techniques using Python
- Implement k-means, hierarchical, and DBSCAN algorithms on sample datasets
- Use visualization techniques to interpret clustering results
Dimensionality Reduction with t-SNE
Show steps
Explore dimensionality reduction techniques by following a guided tutorial on t-SNE.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Follow a step-by-step guide on implementing t-SNE in Python
- Apply t-SNE to a high-dimensional dataset and visualize the results
- Compare t-SNE with PCA for dimensionality reduction
Develop a case study on anomaly detection
Show steps
Enhances practical understanding and critical thinking skills by applying anomaly detection techniques to real-world scenarios.
Browse courses on
Anomaly Detection
Show steps
Show steps
Show steps
- Identify a suitable dataset with anomalies
- Select and implement appropriate anomaly detection algorithms
- Analyze the results and interpret the detected anomalies
- Summarize the findings and present them as a case study
Marketing Segmentation Project
Show steps
Apply unsupervised learning techniques to segment customers and develop a targeted marketing strategy.
Browse courses on
Customer Segmentation
Show steps
Show steps
Show steps
- Collect and preprocess customer data
- Cluster customers using appropriate algorithms
- Analyze clustering results to identify customer segments
- Develop targeted marketing strategies for each customer segment
Kaggle Anomaly Detection Competition
Show steps
Participate in a Kaggle competition to test your skills in anomaly detection and gain practical experience.
Browse courses on
Anomaly Detection
Show steps
Show steps
Show steps
- Explore the competition dataset and familiarize yourself with the task
- Research and implement various anomaly detection algorithms
- Tune your models and submit your predictions for evaluation
- Analyze the results and learn from the feedback provided by the competition organizers
Career center
Learners who complete Unsupervised Learning and Its Applications in Marketing will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists leverage unsupervised learning techniques to extract valuable insights from vast and complex datasets. This course in Unsupervised Learning and Its Applications in Marketing provides you with a strong foundation in unsupervised learning algorithms, enabling you to apply these techniques to real-world marketing scenarios. By mastering unsupervised learning, you can uncover hidden patterns, identify customer segments, and make data-driven decisions that drive business growth.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers play a crucial role in building and deploying machine learning models. This course in Unsupervised Learning and Its Applications in Marketing provides you with hands-on experience in implementing unsupervised learning algorithms in Python, a widely used programming language in machine learning. You will gain the knowledge and skills to design, develop, and evaluate unsupervised learning models for various marketing applications.
Marketing Analyst
Marketing Analyst
Marketing Analysts utilize data analysis techniques to understand consumer behavior and optimize marketing strategies. This course in Unsupervised Learning and Its Applications in Marketing equips you with advanced unsupervised learning techniques that enable you to uncover hidden insights and make data-driven decisions. By leveraging unsupervised learning, you can gain a deeper understanding of customer segmentation, identify growth opportunities, and improve overall marketing effectiveness.
Product Manager
Product Manager
Product Managers are responsible for defining, developing, and launching new products or features. This course in Unsupervised Learning and Its Applications in Marketing provides you with the knowledge and skills to leverage unsupervised learning techniques to understand customer needs, identify market opportunities, and develop innovative products that meet the demands of the market.
Analytics Consultant
Analytics Consultant
Analytics Consultants provide data analysis and consulting services to businesses. This course in Unsupervised Learning and Its Applications in Marketing enhances your data analysis skills by introducing you to unsupervised learning techniques. You will learn how to apply these techniques to marketing data, enabling you to provide more valuable insights and solutions to your clients.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data analysis techniques to provide insights that drive business decisions. This course in Unsupervised Learning and Its Applications in Marketing enhances your data analysis skills by introducing you to unsupervised learning techniques. You will learn how to apply these techniques to extract hidden patterns and insights from marketing data, enabling you to make more informed business decisions.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research to understand market trends and customer behavior. This course in Unsupervised Learning and Its Applications in Marketing provides you with the skills to use unsupervised learning techniques to analyze market data, identify emerging trends, and develop actionable insights for marketing strategies.
Social Media Analyst
Social Media Analyst
Social Media Analysts use data analysis techniques to measure the effectiveness of social media campaigns. This course in Unsupervised Learning and Its Applications in Marketing equips you with the skills to use unsupervised learning techniques to analyze social media data, understand audience behavior, and optimize campaign performance.
Digital Marketing Manager
Digital Marketing Manager
Digital Marketing Managers are responsible for developing and executing digital marketing campaigns. This course in Unsupervised Learning and Its Applications in Marketing equips you with the skills to use unsupervised learning techniques to analyze digital marketing data, understand customer behavior, and optimize campaign performance. By leveraging unsupervised learning, you can gain a competitive edge in the digital marketing landscape.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers use statistical and mathematical methods to analyze data. This course in Unsupervised Learning and Its Applications in Marketing provides you with a strong foundation in unsupervised learning algorithms, enabling you to analyze large and complex marketing datasets and extract valuable insights.
User Experience Researcher
User Experience Researcher
User Experience Researchers focus on improving the experience of users interacting with products or services. This course in Unsupervised Learning and Its Applications in Marketing equips you with the skills to use unsupervised learning techniques to analyze user data, identify pain points, and develop strategies to enhance user experience.
Customer Success Manager
Customer Success Manager
Customer Success Managers focus on building and maintaining long-term customer relationships. This course in Unsupervised Learning and Its Applications in Marketing equips you with the skills to use unsupervised learning techniques to analyze customer data, identify potential churn risks, and develop targeted strategies to improve customer satisfaction and retention.
Database Administrator
Database Administrator
Database Administrators maintain and manage databases. This course in Unsupervised Learning and Its Applications in Marketing may be useful for Database Administrators who are interested in using unsupervised learning techniques to improve data quality, identify anomalies, and optimize database performance.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course in Unsupervised Learning and Its Applications in Marketing may be useful for Software Engineers who are interested in using unsupervised learning techniques to develop machine learning models for marketing applications.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations for investments. This course in Unsupervised Learning and Its Applications in Marketing may be useful for Financial Analysts who are interested in using unsupervised learning techniques to identify investment opportunities and manage risk.
For more career information including salaries, visit:
OpenCourser.com/course/ix0j72/unsupervised
Reading list
We've selected seven 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
Unsupervised Learning and Its Applications in Marketing.
This comprehensive textbook covers the fundamental concepts and algorithms of unsupervised learning, including clustering, dimensionality reduction, and anomaly detection. It provides a solid theoretical foundation and practical guidance for applying unsupervised learning techniques to real-world problems.
Provides an in-depth overview of clustering algorithms, including k-means, hierarchical clustering, and density-based clustering. It covers both theoretical and practical aspects of clustering, making it a valuable resource for practitioners and researchers.
Covers the fundamental concepts and algorithms of dimensionality reduction, including principal component analysis, singular value decomposition, and manifold learning. It provides a comprehensive overview of dimensionality reduction techniques and their applications in machine learning.
Provides an in-depth overview of anomaly detection algorithms, including statistical methods, clustering-based approaches, and machine learning-based techniques. It covers both theoretical and practical aspects of anomaly detection, making it a valuable resource for practitioners and researchers.
Provides a comprehensive overview of autoencoders, a type of neural network that can be used for unsupervised learning. It covers the theoretical foundations of autoencoders, as well as practical guidance for building and training autoencoders for a variety of tasks.
Provides a comprehensive overview of association rule mining, a technique for discovering relationships between items in a dataset. It covers the fundamental concepts of association rule mining, as well as practical guidance for implementing association rule mining algorithms.
Save
Provides a practical guide to association rule mining for marketing professionals. It covers the fundamental concepts of association rule mining, as well as practical guidance for applying association rule mining to marketing problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ix0j72/unsupervised
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Effort
4 weeks of study / 5-6 hours per week
Level
Introductory
Via
Coursera
Institution
O.P. Jindal Global University
Instructor
Ambica Ghai
Language
English
Transcript
tiếng Việt
This course belongs to a
collection
called
Machine Learning for Marketing. It's comprised of five courses:
- Supervised Learning and Its Applications in Marketing
- Digital Marketing Analytics
- Introduction to Decision Science for Marketing
- Unsupervised Learning and Its Applications in Marketing (viewing)
- Text Mining for Marketing
Good to know
Taught by Ambica Ghai, who is recognized for their work in unsupervised learning
Develops skills in association rule mining, dimensionality reduction, and clustering
Combines theory with hands-on exercises and real-world examples
Uses popular programming language Python for practical implementation
Requires basic understanding of Python as a prerequisite
Assumes learners have access to Anaconda navigator software
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