Python for Marketing Analytics: ML projects on Retail Marketing
Machine Learning Algorithms | Numpy, Pandas, Sklearn, VarClusHi | 2 Real World Marketing Analytics Python Projects
Updated on Jun, 2025
Language - English
Development ,Data Science,Python
Lectures -142
Resources -1
Duration -25 hours
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Course Description
Are you interested in learning Machine Learning and start a career in Data Science? Are you interested in developing a project portfolio to showcase your work in the field of Retail Marketing Analytics? How about actionable insights from your machine learning projects that you can show on your resume? Well, then you are right place and this is just the course you need!
This course is designed by a highly experienced data scientist to improve your understanding of Machine Learning and how the concepts can be applied in the field or Retail Analytics.
The course achieves the following 4 objectives:
1. Get started on your learning journey in the field of Data Science and Machine Learning
2. Build your confidence level through a simple explanation of complex theories and corresponding Python Implementation
4. Explain the concepts of Marketing Analytics
3. Help you build a strong body of work through 2 real-life Machine Learning projects in Retail Marketing Domain and build a narrative around insights and recommendations
`The course is structured across the following modules:
1. Fundamentals of Machine Learning
2. Data PreProcessing
3. Feature Selection
4. Clustering Algorithms
5. Dimensionality Reduction
6. Regression Algorithms
7. Classification Algorithms
8. Model Optimization
9. Marketing Analytics Concepts
10. Retail Domain Marketing Analytics Projects
In addition, there are quizzes provided throughout the course to enable quick revision of concepts and generated more confidence in the learning process. All the codes and datasets are provided as downloadable resources
Goals
Build strong understand of fundamental algorithms for Machine Learning and how to apply them in the context of the problem statement.
Prerequisites
Basics of Python
Basics of Statistics
Desire to Learn
Curriculum
Check out the detailed breakdown of what’s inside the course
Course Introduction
1 Lectures
-
Introduction
06:54
06:54
Machine Learning Express Code Folder
1 Lectures
Setting up the environment
1 Lectures
Fundamentals of Machine Learning
5 Lectures
Data Pre Processing
15 Lectures
Feature Selection
3 Lectures
Clustering Algorithms
10 Lectures
Dimensionality Reduction
8 Lectures
Regression Algorithms
23 Lectures
Classification Algorithms
42 Lectures
Model Optimization
3 Lectures
Marketing Analytics Concepts
8 Lectures
Retail Domain Business Project 1 - Response Modeling and Targeting Strategy for Supermarkets
11 Lectures
Retail Domain Business Project 2 - Loyalty Modeling and Engagement Strategy for Organic Product
10 Lectures
Congratulations and Best Wishes
1 Lectures
Instructor Details
Machine Learning Express
Dear Learner,
Welcome aboard the Machine Learning Express !
Machine Learning Express is an online learning platform for building Data Science, Machine Learning and Quantitative skill sets. The courses from Machine Learning Express will simplify challenging concepts and help you develop the skills required to function as an effective Data Scientist. These courses are designed to not only build conceptual clarity and Python skills for Data Science, but also develop a art of communicating a Machine Learning outcome , as a strategic input, to the business community.
The author of the course is a highly experienced Data Scientist Leader. He has worked in the field of analytics and machine learning for the past 15 + years with leading Banks and Insurance companies. He has developed solutions to a multiple challenging business problems by leveraging, data, machine learning and effective communication. He has trained multiple batches of Data Scientists are different organizations. Over the years, he has built many team of analysts from scratch and deliver on problems related to marketing, sales, credit risk, and customer engagement
Wish you all the best in your learning journey .
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