Supervised Learning and Its Applications in Marketing
- Offered byCoursera
Supervised 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 |
Supervised Learning and Its Applications in Marketing at Coursera Highlights
- Earn a certificate from O.P. Jindal Global University
- Add to your LinkedIn profile
- 36 quizzes
Supervised Learning and Its Applications in Marketing at Coursera Course details
- What you'll learn
- Apply Python as an effective tool for supervised learning techniques.
- Develop and train supervised machine learning models for classification and regression tasks.
- Interpret and analyze various applications of supervised learning in marketing.
- Describe the deployment of machine learning models and the challenges encountered in the deployment.
- Welcome to the Supervised Learning and Its Applications in Marketing course! Supervised learning is the process of making an algorithm to learn to map an input to a particular output. Supervised learning algorithms can help make predictions for new unseen data. In this course, you will use the Python programming language, which is an effective tool for machine learning applications. You will be introduced to the supervised learning techniques: regression and classification. The course will focus on the applications of these techniques in the domain of marketing.
- With the growing amount of data and applications of machine learning in marketing, we can easily find examples of the usage of machine learning in marketing efforts. Companies are starting to use machine learning to better understand customer behaviors and identify different customer segments based on their activity patterns. Many organizations also use machine learning to predict future customer behaviors, such as what items they are likely to purchase, which websites they are likely to visit, and who are likely to churn. With endless use cases of machine learning for marketing, companies of all sizes can benefit from using machine learning for their marketing efforts.
- To succeed in this course, you should have a basic understanding of Python.
- You will also need certain software requirements, including an Anaconda navigator.
Supervised Learning and Its Applications in Marketing at Coursera Curriculum
Introduction to Supervised Learning in Marketing
Course Intro video
Major Challenges Marketers Face Today
Introduction to Machine Learning for Marketing
Concepts for Machine Learning in Marketing
Introduction to Supervised Learning in Marketing
Course Overview
Essential Reading: Major Challenges Marketers Face Today
Essential Reading: Introduction to Machine Learning for Marketing
Essential Reading: Concepts for Machine Learning in Marketing
Essential Reading: Introduction to Supervised Learning in Marketing
Major Challenges Marketers Face Today
Introduction to Machine Learning for Marketing
Concepts for Machine Learning in Marketing
Introduction to Supervised Learning in Marketing
Understanding the Applications of Supervised Learning in Marketing
Getting Started With Supervised Learning in Marketing
Problem Workflow for Supervised Learning and Its Techniques
Key Performance Indicators and Visualizations
Drivers Behind Marketing Engagement
Decision Trees
Essential Reading: Problem Workflow for Supervised Learning and Its Techniques
Essential Reading: Key Performance Indicators and Visualizations
Essential Reading: Drivers Behind Marketing Engagement
Essential Reading: Decision Trees
Problem Workflow for Supervised Learning and Its Techniques
Key Performance Indicators and Visualizations
Drivers Behind Marketing Engagement
Decision Trees
Weekly Summative Assessment: Supervised Learning in Marketing
Deriving Insights from Data
From Engagement to Conversion
Interpreting Decision Trees
Importance of Product Analytics
Product Analytics Using Python
Essential Reading: From Engagement to Conversion
Essential Reading: Interpreting Decision Trees
Essential Reading: Importance of Product Analytics
Essential Reading: Product Analytics Using Python
From Engagement to Conversion
Interpreting Decision Trees
Importance of Product Analytics
Product Analytics Using Python
Product Recommender System
Product Recommender System
Collaborative Filtering
Building Product Recommendation Engine Using Python
Item-Based Collaborative Filtering and Recommendations
Essential Reading: Product Recommender System
Essential Reading: Collaborative Filtering
Essential Reading: Building Product Recommendation Engine Using Python
Essential Reading: Item-Based Collaborative Filtering and Recommendations
Product Recommender System
Collaborative Filtering
Building Product Recommendation Engine Using Python
Item-Based Collaborative Filtering and Recommendations
Application of Supervised Learning in Product Recommender System
Weekly Summative Assessment: Deriving Insights from Data and Product Recommender System
Personalized Marketing
Understanding Customer Behavior
Conducting Customer Analytics with Python
Predictive Analytics in Marketing
Predicting the Likelihood of Marketing Engagement Using Python
Essential Reading: Understanding Customer Behavior
Essential Reading: Conducting Customer Analytics with Python
Essential Reading: Predictive Analytics in Marketing
Essential Reading: Predicting the Likelihood of Marketing Engagement Using Python
Understanding Customer Behavior
Conducting Customer Analytics with Python
Predictive Analytics in Marketing
Predicting the Likelihood of Marketing Engagement Using Python
Supervised Learning to Personalize Marketing and Build Strategies
Customer Lifetime Value
Customer Lifetime Value
Evaluating Regression Models
Predicting the Three-Month CLV with Python: Part I
Predicting the Three-Month CLV with Python: Part II
Essential Reading: Customer Lifetime Value
Essential Reading: Evaluating Regression Models
Essential Reading: Predicting the Three-Month CLV with Python: Part I
Essential Reading: Predicting the Three-Month CLV with Python: Part II
Customer Lifetime Value
Evaluating Regression Models
Predicting the Three-Month CLV with Python: Part I
Predicting the Three-Month CLV with Python: Part II
Customer Churn Prediction Using Supervised Learning
Weekly Summative Assessment: Personalized Marketing and Customer Lifetime Value
Retaining Customers
Customer Retention
Artificial Neural Networks (ANNs)
Predicting Customer Churn with Python: Part I
Predicting Customer Churn with Python: Part II
Essential Reading: Customer Retention
Essential Reading: Artificial Neural Networks (ANNs)
Essential Reading: Predicting Customer Churn with Python: Part I
Essential Reading: Predicting Customer Churn with Python: Part II
Customer Retention
Artificial Neural Networks (ANNs)
Predicting Customer Churn with Python: Part I
Predicting Customer Churn with Python: Part II
Deployment of Supervised Learning Models
Real-Life Challenges in Applying Supervised Learning Models
Standardized Framework for Success
Industry Views on AI strategy
Future Scope
Essential Reading: Real-Life Challenges in Applying Supervised Learning Models
Essential Reading: Standardized Framework for Success
Essential Reading: Industry Views on AI strategy
Essential Reading: Future Scope
Real-Life Challenges in Applying Supervised Learning Models
Standardized Framework for Success
Industry Views on AI strategy
Future Scope
Weekly Summative Assessment: Retaining customers and Deployment of Supervised Learning Models
Course Wrap-Up Video
Graded Quiz: Retaining ustomers and Deployment of Supervised Learning Models
Supervised Learning and Its Applications in Marketing at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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