Analytics
Exploratory Data Analysis
Machine Learning for Retail
This course will explore the conceptual aspects of applying machine learning to problems in the retail industry, discuss case studies of machine learning used by retailers, and explore practical implementations of techniques on real-world data.
Pluralsight Teams₹4,166.75 per user/month, billed annually
₹50,004
CompleteBilled Monthly
₹2,370/mo
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Course Insight
Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.
Beginner FriendlySelf-Paced LearningHands-On Learning
SKILLS TO
MASTER
ModelingTrending
Predictive Machine LearningSQL Querying
Relational Data ManagementCareers:Relevant for professionals pursuing roles within Data Science.
💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.
Quick Facts
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What You’ll Learn
- Course Overview : 2mins.
- Exploring Applications of Machine Learning in Retail : 41mins.
- Case Study: Optimizing Product Prices Using Machine Learning : 21mins.
- Case Study: Optimizing Supply Planning Using Machine Learning : 18mins.
- Applying Machine Learning Techniques to Retail Data : 37mins.
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Description
The retail industry has been at the cutting edge of applying quantitative techniques in order to relentlessly optimize operations. AI is also extensively used in retail to improve customer experiences making customer interactions less transactional and more personalized.
In this course, Machine Learning for Retail, you'll explore machine learning techniques currently applied in the retail industry.
First, you'll look at what a Gartner report has to say about the future of AI in the retail industry and you will explore some examples and cases of where ML is already being used in retail - for predicting customer behavior, for visual and voice search, for price and inventory predictions for customer behavior tracking.
Then, you'll also get an intuitive understanding of how visual search works, using convolutional neural networks and similarity algorithms.
Next, you'll explore two ML case studies from research papers - the first one discusses how an online e-commerce platform used a price optimization model to set prices across products on its platform to maximize the platform's revenue and gross margin. The second case study explores the dynamic vehicle routing problem in the supply chain industry and sees how machine learning techniques can help find good solutions to this problem.
Finally, you will get hands-on coding and see how you can use the apriori algorithm and market basket analysis to analyze customer transaction data.
When you are finished with this course you will have the awareness of how machine learning can be applied in the retail industry and hands-on experience working with retail data.
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