Machine Learning for Retail from Pluralsight

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.

What's inside

Syllabus

Course Overview

Exploring Applications of Machine Learning in Retail

Case Study: Optimizing Product Prices Using Machine Learning

Case Study: Optimizing Supply Planning Using Machine Learning

Applying Machine Learning Techniques to Retail Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills and techniques used by professionals in the retail industry

Uses hands-on lab work and interactive materials

Is taught by Janani Ravi, who are recognized for their work in machine learning

Requires learners to come in with prior programming experience

Provides case studies of machine learning used by retailers

Covers how machine learning can be used for tasks such as predicting customer behavior and inventory levels

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning for Retail with these activities:

Review statistical concepts

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Strengthen your statistical foundation to support your understanding of machine learning models and their evaluation.

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Review lecture notes or textbooks on statistical concepts
Complete practice problems or online tutorials

Brush up on machine learning basics

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Gain a strong foundation to build upon as you progress through the course.

Browse courses on Machine Learning

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Review lecture notes or textbooks on machine learning concepts
Complete practice problems or online tutorials

Join a study group or discussion forum on machine learning in retail

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Engage with peers, share knowledge, and gain diverse perspectives to enhance your understanding.

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Find or create a study group or online discussion forum
Participate in discussions, ask questions, and share insights
Collaborate on projects or assignments

Five other activities

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Follow tutorials on applying machine learning in retail

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Develop practical skills and gain insights into real-world applications of machine learning in the retail industry.

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Identify reputable online resources or platforms offering tutorials
Follow step-by-step instructions and complete hands-on exercises
Apply what you learn to retail-specific scenarios

Practice implementing machine learning algorithms

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Strengthen your understanding and coding proficiency in applying machine learning techniques.

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Choose a programming language and relevant libraries
Solve coding challenges or work on small projects involving machine learning algorithms
Debug and refine your code to improve accuracy and efficiency

Assist with data analysis or machine learning projects in the retail industry

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Gain hands-on experience and contribute to the field while applying your knowledge in a practical setting.

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Identify opportunities to volunteer with retail companies or organizations
Offer your assistance with data analysis or machine learning tasks
Collaborate with experts and learn from their insights

Build a machine learning model for a retail problem

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Apply your knowledge to solve a real-world problem and showcase your understanding of machine learning in retail.

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Define a specific retail problem to address
Collect and preprocess relevant data
Train and evaluate a machine learning model
Deploy and monitor the model

Participate in hackathons or competitions related to machine learning in retail

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Challenge yourself, showcase your skills, and gain recognition while applying your knowledge in a competitive environment.

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Identify relevant hackathons or competitions
Form a team or participate individually
Develop innovative solutions to retail-related problems using machine learning
Present your work and compete for awards

Career center

Learners who complete Machine Learning for Retail will develop knowledge and skills that may be useful to these careers:

Retail Analyst

A Retail Analyst focuses on analyzing data to understand consumer behavior and market trends in the retail industry. This role requires a deep understanding of retail operations and the ability to use data to identify opportunities for growth and improvement. The course's exploration of machine learning applications in retail, combined with the case studies and hands-on coding, would provide you with the necessary knowledge and skills to succeed as a Retail Analyst. You would be well-equipped to leverage machine learning techniques to extract insights from retail data, enabling you to make informed decisions and drive business outcomes.

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Machine Learning Engineer

A Machine Learning Engineer uses machine learning techniques to solve complex business problems. This role requires a deep understanding of the concepts and algorithms involved in machine learning. The course's exploration of conceptual aspects of machine learning, as well as case studies and hands-on coding, would significantly contribute to your success in this role. By taking this course, you'll gain the necessary knowledge and skills to design, implement, and evaluate machine learning solutions for retail-specific problems.

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Data Scientist

In your role as a Data Scientist, you would use mathematical and statistical techniques to analyze data from various sources, including surveys, experiments, and observational studies. The course's exploration of machine learning's conceptual aspects and case studies of its use in retail would provide you with a solid foundation for this role. Additionally, the hands-on coding experience in the course would equip you with the practical skills needed to apply machine learning techniques to real-world retail data.

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Data Mining Specialist

A Data Mining Specialist extracts valuable patterns and insights from large datasets. This role requires a deep understanding of data mining techniques and algorithms, as well as the ability to communicate insights effectively. The course's exploration of machine learning applications in retail, particularly in market basket analysis and customer transaction data analysis, would provide you with valuable insights into how to leverage data mining to drive business intelligence and decision-making.

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Statistician

A Statistician collects, analyzes, and interprets data to provide insights and make predictions. This role requires a deep understanding of statistical methods and the ability to communicate findings effectively. The course's emphasis on the practical applications of machine learning in retail, combined with the case studies and hands-on coding, would provide you with valuable insights into how to leverage statistical techniques to drive data-driven decision-making in the retail industry.

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Supply Chain Analyst

As a Supply Chain Analyst, you would be responsible for analyzing and improving the efficiency of supply chain operations. This role requires an understanding of logistics, inventory management, and the ability to use data to identify and solve problems. The course's exploration of machine learning applications in retail, particularly in supply chain optimization and dynamic vehicle routing, would provide you with valuable insights into how to leverage data to drive supply chain performance.

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Software Engineer

A Software Engineer designs, develops, and implements software systems. This role requires a strong understanding of computer science fundamentals and the ability to work in a team environment. The course's hands-on coding experience, combined with its exploration of machine learning algorithms and applications, would provide you with a valuable foundation for a career as a Software Engineer in the retail industry. You would be well-equipped to develop and implement machine learning solutions to solve complex business challenges.

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Marketing Manager

In your role as a Marketing Manager, you would be responsible for developing and executing marketing strategies to promote products and services. This role requires an understanding of consumer behavior and market trends, as well as the ability to use data to measure and evaluate marketing campaigns. The course's exploration of machine learning applications in retail, particularly in predicting customer behavior and personalizing customer interactions, would provide you with valuable insights into how to leverage data to drive marketing strategies.

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Market Researcher

A Market Researcher conducts research to understand consumer behavior and market trends. This role requires an understanding of research methods and the ability to analyze and interpret data. The course's exploration of machine learning applications in retail, particularly in customer behavior prediction and personalization, would provide you with valuable insights into how to leverage machine learning techniques to conduct market research and drive customer-centric strategies.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data and make investment decisions. This role requires a deep understanding of financial markets and the ability to develop and implement quantitative models. The course's exploration of machine learning applications in retail, particularly in price and inventory predictions, would provide you with valuable insights into how to leverage quantitative techniques to drive investment strategies.

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Data Governance Specialist

A Data Governance Specialist ensures the quality, consistency, and security of data across an organization. This role requires an understanding of data governance policies and procedures, as well as the ability to work with stakeholders to implement and enforce data governance initiatives. The course's emphasis on ethical considerations in machine learning, as well as the hands-on experience in working with real-world retail data, would provide you with valuable insights into the challenges and best practices of data governance in the retail industry.

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Business Analyst

As a Business Analyst, you would use analytical skills to identify and solve business problems. This role involves gathering and interpreting data to make recommendations for improvements. The course's emphasis on the practical applications of machine learning in retail would provide you with valuable insights into how to leverage data to drive business decisions. The case studies and hands-on coding experience would also equip you with the skills to analyze and interpret retail data effectively.

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Customer Experience Manager

A Customer Experience Manager oversees the customer experience across all touchpoints, including retail stores, online channels, and customer service. This role requires an understanding of customer behavior and market trends, as well as the ability to use data to improve customer satisfaction. The course's exploration of machine learning applications in retail, particularly in customer behavior prediction and personalization, would provide you with valuable insights into how to leverage data to drive customer-centric strategies.

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Product Manager

As a Product Manager, you would be responsible for managing the development and launch of new products or services. This role requires an understanding of consumer needs and market trends, as well as the ability to use data to make informed decisions. The course's exploration of machine learning applications in retail, including price and inventory predictions, would provide you with valuable insights into how to leverage data to drive product development and marketing strategies.

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Operations Manager

An Operations Manager oversees the day-to-day operations of a business, including supply chain management, inventory control, and customer service. This role requires an understanding of business processes and the ability to use data to improve efficiency and productivity. The course's exploration of machine learning applications in retail, particularly in supply chain optimization and dynamic vehicle routing, would provide you with valuable insights into how to leverage data to drive operational excellence.

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