Unsupervised Learning and Its Applications in Marketing
- Offered byCoursera
Unsupervised Learning and Its Applications in Marketing at Coursera Overview
Duration | 21 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Unsupervised Learning and Its Applications in Marketing at Coursera Highlights
- Earn a certificate from O.P. Jindal Global University
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Unsupervised Learning and Its Applications in Marketing at Coursera Course details
- What you'll learn
- Apply Python as an effective tool for implementing various algorithms.
- Describe unsupervised learning and list its various algorithms.
- List the various applications and promising areas for the application of unsupervised learning.
- 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.
Unsupervised Learning and Its Applications in Marketing at Coursera Curriculum
Fundamentals of Unsupervised Learning
Course Introduction
Introduction to Unsupervised Learning
Unsupervised Algorithms: Part I
Unsupervised Algorithms: Part II
Applications of Unsupervised Learning
Course Overview
Essential Reading: Introduction to Unsupervised Learning
Essential Reading: Unsupervised Algorithms: Part I
Essential Reading: Unsupervised Algorithms: Part II
Essential Reading: Applications of Unsupervised Learning
Introduction to Unsupervised Learning
Unsupervised Algorithms: Part I
Unsupervised Algorithms: Part II
Applications of Unsupervised Learning
Understanding the Applications of Unsupervised Learning in Marketing
Clustering and Its Types
Clustering
k-means
Hierarchical Clustering
DBSCAN
Essential Reading: Clustering
Essential Reading: k-Means
Essential Reading: Hierarchical Clustering
Essential Reading: DBSCAN
Clustering
k-means
Hierarchical Clustering
DBSCAN
Weekly Summative Assessment: Fundamentals of Unsupervised Learning and Clustering
Data-Driven Customer Segmentation
Customer Segmentation
Segmenting Customers with Python: Part I
Segmenting Customers with Python: Part II
Introduction to Dimensionality Reduction
Essential Reading: Customer Segmentation
Essential Reading: Segmenting Customers with Python: Part I
Essential Reading: Segmenting Customers with Python: Part II
Essential Reading: Introduction to Dimensionality Reduction
Customer Segmentation
Segmenting Customers with Python: Part I
Segmenting Customers with Python: Part II
Introduction to Dimensionality Reduction
Applications of Unsupervised Learning in Customer Segmentation
Dimensionality Reduction
Dimensionality Reduction Algorithm – Linear Projection Techniques
Dimensionality Reduction Algorithm – Manifold Learning
Other Dimensionality Reduction Methods
Introduction to Anomaly Detection
Essential Reading: Dimensionality Reduction Algorithm – Linear Projection Techniques
Essential Reading: Dimensionality Reduction Algorithm – Manifold Learning
Essential Reading: Other Dimensionality Reduction Methods
Essential Reading: Introduction to Anomaly Detection
Dimensionality Reduction Algorithm – Linear Projection Techniques
Dimensionality Reduction Algorithm – Manifold Learning
Other Dimensionality Reduction Methods
Introduction to Anomaly Detection
Weekly Summative Assessment: Data-Driven Customer Segmentation and Dimensionality Reduction
Anomaly Detection
Normal PCA Anomaly Detection
Sparse and Kernel PCA Anomaly Detection
Random Projection Anomaly Detection
Nonlinear Anomaly Detection
Essential Reading: Normal PCA Anomaly Detection
Essential Reading: Sparse and Kernel PCA Anomaly Detection
Essential Reading: Random Projection Anomaly Detection
Essential Reading: Nonlinear Anomaly Detection
Normal PCA Anomaly Detection
Sparse and Kernel PCA Anomaly Detection
Random Projection Anomaly Detection
Nonlinear Anomaly Detection
Autoencoders and Association Learning
Introduction to Autoencoders
Types of Autoencoders
Market Basket Analysis: Part 1
Market Basket Analysis: Part 2
Essential Reading: Introduction to Autoencoders
Essential Reading: Types of Autoencoders
Essential Reading: Market Basket Analysis: Part 1
Essential Reading: Market Basket Analysis: Part 2
Introduction to Autoencoders
Types of Autoencoders
Market Basket Analysis: Part 1
Market Basket Analysis: Part 2
Application of Unsupervised Learning for Market Basket Analysis
Weekly Summative Assessment: Anomaly Detection, Autoencoders, and Association Learning
Semi-Supervised Learning
Semi-Supervised Learning
Supervised model
Unsupervised Model
Semi-Supervised Model
Essential Reading: Semi-Supervised Learning
Essential Reading: Supervised model
Essential Reading: Unsupervised Model
Essential Reading: Semi-Supervised Model
Semi-Supervised Learning
Supervised model
Unsupervised Model
Semi-Supervised Model
Semisupervised Learning and Its Applications in Marketing
Recommender systems Using RBM
Boltzmann Machines
Recommender Systems
Collaborative Filtering Using RBMs
Future of Unsupervised Learning
Essential Reading: Boltzmann Machines
Essential Reading: Recommender Systems
Essential Reading: Collaborative Filtering Using RBMs
Essential Reading: Future of Unsupervised Learning
Boltzmann Machines
Recommender Systems
Collaborative Filtering Using RBMs
Future of Unsupervised Learning
Weekly Summative Assessment: Semi-Supervised Learning and Recommender systems Using RBM
Course Wrap-up
Graded Quiz: Semi-Supervised Learning and Recommender systems Using RBM
Unsupervised Learning and Its Applications in Marketing at Coursera Admission Process
Important Dates
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