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.
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.
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.
