We may earn an affiliate commission when you visit our partners.
Rajvir Dua
and
Neelesh Tiruviluamala
In this course, we’ll learn about more advanced machine learning methods that are used to tackle problems in the supply chain. We’ll start with an overview of the different ML paradigms (regression/classification) and where the latest models fit into these breakdowns. Then, we’ll dive deeper into some of the specific techniques and use cases such as using neural networks to predict product demand and random forests to classify products. An important part to using these models is understanding their assumptions and required preprocessing steps. We’ll end with a project incorporating advanced techniques with an image classification problem to find faulty products coming out of a machine.
Register for this course and see more details by visiting:
OpenCourser.com/course/bz95wk/advanced
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Machine Learning in the Supply Chain
In this module, we'll learn about the use cases of machine learning in the supply chain. We'll start with the big picture applications before diving deeper into specific algorithms, including neural networks. Throughout the module, we'll explain not only the general artificial intelligence concepts and mathematics, but also how these algorithms can specifically be used for the supply chain.
Read more
Syllabus
Machine Learning in the Supply Chain
In this module, we'll learn about the use cases of machine learning in the supply chain. We'll start with the big picture applications before diving deeper into specific algorithms, including neural networks. Throughout the module, we'll explain not only the general artificial intelligence concepts and mathematics, but also how these algorithms can specifically be used for the supply chain.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Examines real world supply chain problems and use cases of AI and ML in solving these issues
Explores advanced AI concepts namely neural networks, image analysis, and natural language processing
Teaches practical considerations of coding using libraries and frameworks
Taught by Neelesh Tiruviluamala, an instructor with experience in supply chain management and data science
Provides hands-on labs and downloadable resources that cover majority of the course concepts
Assumes knowledge of Python and basic computer science concepts
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Practical advanced ai for supply chain
According to learners, this course is a highly practical offering for professionals seeking to apply advanced AI in supply chain. Many found the real-world applicability and the anomaly detection project particularly valuable. Students praised the clear instruction, enabling confident skill integration. However, some with extensive AI backgrounds felt the 'advanced' material was more intermediate-level, occasionally requiring additional research. There were also suggestions for more hands-on exercises and robust code samples.
Instructors offer clear explanations and valuable resources.
"I felt the instructors were very knowledgeable."
"The instructors provided clear explanations and useful resources."
"The concepts of neural networks and NLP were introduced clearly."
A practical, challenging, and highly useful capstone.
"The final project on anomaly detection for product defects was incredibly practical."
"The project was challenging but highly rewarding."
"The practical examples and the final project were incredibly helpful."
"I found the image classification project on detecting anomalies to be very relevant and a great way to solidify my skills."
Highly applicable to real-world supply chain challenges.
"Absolutely fantastic course! The way they explained neural networks for demand prediction was crystal clear and directly applicable to my job in logistics."
"As someone deeply involved in supply chain optimization, the practical applications of AI techniques were invaluable."
"I learned so much that I could immediately use. Highly recommend for supply chain professionals."
"Excellent course for applying AI to real-world supply chain problems. I'm now confidently applying some of these techniques at work."
Could benefit from more thorough mathematical explanations.
"Some of the mathematical foundations could have been a bit more thoroughly explained for someone coming from a non-ML background."
"I needed to review external resources to fully grasp the underlying math behind certain algorithms."
Desire for more hands-on activities and robust code.
"I wish there were more hands-on labs for the initial setup of these models."
"The provided code was a bit messy."
"The code examples were not always robust, and I encountered several errors."
"I would have preferred more opportunities for direct coding practice with clear, error-free examples."
Perceived depth varies, some found it more intermediate.
"I was expecting more *advanced* material. Much of it felt like a basic ML refresher with some supply chain buzzwords."
"The depth felt lacking in certain areas, especially for 'advanced' techniques. I had to do a lot of supplementary research to fully grasp some topics."
"Some aspects of the 'advanced' claim felt more like an intermediate introduction. More complex case studies would be beneficial."
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 Advanced AI Techniques for the Supply Chain with these
activities:
Organize and review course materials
Show steps
Stay organized and prepare for the course by reviewing and compiling notes, assignments, and other materials.
Show steps
-
Gather all course materials, including notes, assignments, and textbook readings
-
Organize the materials into a logical and accessible structure
Compile a library of machine learning resources
Show steps
Organize and collect valuable resources such as articles, videos, and datasets to support your learning and reference in the future.
Browse courses on
Machine Learning
Show steps
-
Identify and gather relevant resources from various sources
-
Organize the resources into a structured and accessible format
Review applied mathematics
Show steps
Refresh your understanding of applied mathematics concepts, such as linear algebra and calculus, which are essential for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review notes and textbooks from previous math courses
-
Solve practice problems and exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with a mentor in the field of machine learning
Show steps
Seek guidance and support from an experienced professional who can provide personalized advice and insights.
Browse courses on
Machine Learning
Show steps
-
Identify potential mentors through professional networks or online platforms
-
Reach out to mentors and request their guidance
Participate in a machine learning study group
Show steps
Connect with fellow learners and discuss course concepts, share insights, and work through problems together.
Browse courses on
Machine Learning
Show steps
-
Find or create a study group with other students taking the course
-
Meet regularly to discuss course material, ask questions, and collaborate on projects
Build a simple machine learning model
Show steps
Gain hands-on experience by implementing a basic machine learning model, such as a linear regression or decision tree.
Browse courses on
Supervised Learning
Show steps
-
Choose a dataset and define the problem
-
Select and apply an appropriate machine learning algorithm
-
Train and evaluate the model
Complete online courses or tutorials on advanced machine learning
Show steps
Supplement your learning by exploring advanced machine learning concepts through online resources, such as Coursera or edX.
Browse courses on
Deep Learning
Show steps
-
Identify online courses or tutorials that align with your interests and learning goals
-
Follow the course or tutorial diligently, completing assignments and quizzes
Participate in a machine learning hackathon or competition
Show steps
Challenge yourself by applying your skills in a competitive setting and gain valuable experience in solving real-world problems.
Browse courses on
Machine Learning
Show steps
-
Identify and register for a hackathon or competition that aligns with your interests
-
Team up with other participants or work individually
-
Develop a solution to the specified problem statement using machine learning techniques
Organize and review course materials
Show steps
Stay organized and prepare for the course by reviewing and compiling notes, assignments, and other materials.
Show steps
Show steps
Show steps
- Gather all course materials, including notes, assignments, and textbook readings
- Organize the materials into a logical and accessible structure
Compile a library of machine learning resources
Show steps
Organize and collect valuable resources such as articles, videos, and datasets to support your learning and reference in the future.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and gather relevant resources from various sources
- Organize the resources into a structured and accessible format
Review applied mathematics
Show steps
Refresh your understanding of applied mathematics concepts, such as linear algebra and calculus, which are essential for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes and textbooks from previous math courses
- Solve practice problems and exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with a mentor in the field of machine learning
Show steps
Seek guidance and support from an experienced professional who can provide personalized advice and insights.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms
- Reach out to mentors and request their guidance
Participate in a machine learning study group
Show steps
Connect with fellow learners and discuss course concepts, share insights, and work through problems together.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course
- Meet regularly to discuss course material, ask questions, and collaborate on projects
Build a simple machine learning model
Show steps
Gain hands-on experience by implementing a basic machine learning model, such as a linear regression or decision tree.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Choose a dataset and define the problem
- Select and apply an appropriate machine learning algorithm
- Train and evaluate the model
Complete online courses or tutorials on advanced machine learning
Show steps
Supplement your learning by exploring advanced machine learning concepts through online resources, such as Coursera or edX.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify online courses or tutorials that align with your interests and learning goals
- Follow the course or tutorial diligently, completing assignments and quizzes
Participate in a machine learning hackathon or competition
Show steps
Challenge yourself by applying your skills in a competitive setting and gain valuable experience in solving real-world problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for a hackathon or competition that aligns with your interests
- Team up with other participants or work individually
- Develop a solution to the specified problem statement using machine learning techniques
Career center
Learners who complete Advanced AI Techniques for the Supply Chain will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models. This course may be useful for Machine Learning Engineers who want to work on problems in the supply chain. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation, which are all essential for Machine Learning Engineers.
Data Scientist
Data Scientist
Data Scientists use data analysis and machine learning to solve business problems. This course may be useful for Data Scientists who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as neural networks, random forests, and image classification, which are all relevant to the work of Data Scientists in the supply chain.
Logistics Manager
Logistics Manager
Logistics Managers are responsible for the planning and execution of the logistics operations of a company. This course may be useful for Logistics Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their logistics operations. The course covers topics such as transportation, warehousing, and inventory management, which are all relevant to the work of Logistics Managers.
Supply Chain Manager
Supply Chain Manager
Supply Chain Managers are responsible for the planning and execution of the supply chain. This course may be useful for Supply Chain Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of the supply chain. The course covers topics such as inventory management, transportation, and warehousing, which are all relevant to the work of Supply Chain Managers.
Transportation Manager
Transportation Manager
Transportation Managers are responsible for the planning and execution of the transportation operations of a company. This course may be useful for Transportation Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their transportation operations. The course covers topics such as transportation planning, transportation scheduling, and transportation optimization, which are all relevant to the work of Transportation Managers.
Warehouse Manager
Warehouse Manager
Warehouse Managers are responsible for the planning and management of a warehouse. This course may be useful for Warehouse Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their warehouse operations. The course covers topics such as warehouse design, warehouse operations, and warehouse management systems, which are all relevant to the work of Warehouse Managers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. This course may be useful for Operations Research Analysts who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as optimization, simulation, and decision analysis, which are all relevant to the work of Operations Research Analysts in the supply chain.
Inventory Manager
Inventory Manager
Inventory Managers are responsible for the planning and management of inventory for a company. This course may be useful for Inventory Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their inventory management operations. The course covers topics such as inventory forecasting, inventory optimization, and inventory control, which are all relevant to the work of Inventory Managers.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. This course may be useful for Business Analysts who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as data mining, forecasting, and risk analysis, which are all relevant to the work of Business Analysts in the supply chain.
Procurement Manager
Procurement Manager
Procurement Managers are responsible for the sourcing and procurement of goods and services for a company. This course may be useful for Procurement Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their procurement operations. The course covers topics such as supplier management, contract management, and risk management, which are all relevant to the work of Procurement Managers.
Quality Control Manager
Quality Control Manager
Quality Control Managers are responsible for the planning and execution of quality control operations for a company. This course may be useful for Quality Control Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their quality control operations. The course covers topics such as quality control planning, quality control inspection, and quality control analysis, which are all relevant to the work of Quality Control Managers.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying, assessing, and mitigating risks for a company. This course may be useful for Risk Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their risk management operations. The course covers topics such as risk identification, risk assessment, and risk mitigation, which are all relevant to the work of Risk Managers.
Financial Analyst
Financial Analyst
Financial Analysts use data and analysis to help businesses make better financial decisions. This course may be useful for Financial Analysts who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as financial modeling, financial forecasting, and financial risk analysis, which are all relevant to the work of Financial Analysts in the supply chain.
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts use data and modeling to improve the flow of goods, services, and resources within a supply chain. This course may be useful for developing the skills needed to analyze and predict demand, classify products, and identify potential problems in the supply chain using advanced AI techniques. These techniques can help Supply Chain Analysts optimize inventory levels, reduce costs, and improve customer service.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who want to use advanced AI techniques to improve the design and development of new products. The course covers topics such as user research, data analysis, and market research, which are all relevant to the work of Product Managers.
For more career information including salaries, visit:
OpenCourser.com/course/bz95wk/advanced
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Advanced AI Techniques for the Supply Chain.
A comprehensive and highly acclaimed textbook that provides a thorough grounding in statistical learning, including supervised and unsupervised learning methods.
Save
A comprehensive and in-depth guide to deep learning, covering both theoretical foundations and practical applications.
Save
A comprehensive and in-depth guide to computer vision, covering topics such as image processing, feature extraction, and object recognition.
A practical and hands-on guide to deep learning, covering topics such as image classification, natural language processing, and computer vision.
A comprehensive textbook on supply chain management, covering topics such as supply chain strategy, planning, and operations.
A classic textbook that provides a comprehensive overview of machine learning, including advanced topics such as Bayesian inference and kernel methods.
A practical and hands-on guide to machine learning with Python libraries such as Scikit-Learn, Keras, and TensorFlow.
A practical guide to natural language processing, covering topics such as text classification, sentiment analysis, and machine translation.
A foundational book on lean supply chain management, providing insights into the principles and practices of lean.
A practical guide to machine learning with Python, covering both supervised and unsupervised learning methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bz95wk/advanced
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Rating
3.2
Effort
22 hours to complete
Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructors
Rajvir Dua
Neelesh Tiruviluamala
Language
English
This course belongs to a
collection
called
Machine Learning for Supply Chains. It's comprised of four courses:
- Advanced AI Techniques for the Supply Chain (viewing)
- Capstone Project: Predicting Safety Stock
- Demand Forecasting Using Time Series
- Fundamentals of Machine Learning for Supply Chain
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser