Fundamentals of Machine Learning for Supply Chain

Go to Course: https://www.coursera.org/learn/machine-learning-for-supply-chain-fundamentals

Introduction

## Course Review: Fundamentals of Machine Learning for Supply Chain ### Overview The **Fundamentals of Machine Learning for Supply Chain** course offered on Coursera is an exceptional resource aimed at both newcomers to programming and professionals looking to deepen their understanding of how machine learning can be applied in supply chain management. This engaging course leverages the power of Python to navigate complex supply chain datasets, equipping learners with practical skills that are directly applicable to real-world problems. Whether you are a seasoned data scientist or someone simply curious about the intersection of supply chain management and machine learning, this course provides a well-rounded introduction that starts from the ground up. Even if you have no prior knowledge of supply chain fundamentals, the datasets used throughout the course serve as an enlightening canvas that aligns perfectly with the instructional content. ### Syllabus Breakdown **1. Introduction to Programming Concepts and Python Practices** The course kicks off with an essential grounding in programming concepts, focusing on Python. Participants will familiarize themselves with data structures, functions, loops, and the vital skill of importing modules and libraries. This module culminates in hands-on practice where learners will optimize a supply constraint problem through linear programming techniques, ensuring a smooth transition from theory to application. **2. Digging Into Data: Common Tools for Data Science** In the second module, learners delve deeper into Python and Numpy, the foundational tools of data science. The focus here is on manipulating data arrays, loading datasets, and implementing essential data-cleaning techniques. Understanding how to work with dataframes and applying functions like summary statistics and basic visualizations are covered. These activities utilize supply chain datasets, which not only provide context but also relevance for those in the field. **3. Higher Level Data Wrangling and Manipulation** As the course progresses, participants will enhance their Pandas skills, learning about advanced data manipulation techniques such as merging, reshaping, and one-hot encoding. This module introduces critical tools in data preparation necessary for machine learning applications, including the potent Groupby-Apply-Transform method in Pandas. Working through these complexities prepares learners for the intricacies of maintaining and analyzing large datasets. **4. Course 1 Final Project** The course concludes with an engaging final project that brings together the diverse skills learned throughout. Students will work with a collection of datasets, focusing on warehouse capacities, product demands, and freight rates, all to optimize the costs associated with production and shipping of products. This hands-on project solidifies learning and illustrates the practical implications of machine learning within supply chain management. ### Recommendation I highly recommend the **Fundamentals of Machine Learning for Supply Chain** course on Coursera to anyone curious about how machine learning technologies can transform supply chain operations. This course is particularly suited for: - **Professionals in Supply Chain Management**: Looking to leverage data science in optimizing operations. - **Data Science Enthusiasts**: Eager to learn Python within a context that ensures retainable skills. - **Students or Beginners in Programming**: Seeking a structured introduction to programming concepts that can lead into machine learning applications. What sets this course apart is its hands-on approach combined with relevant content, making it not only educational but also immediately applicable. You’ll walk away not just with theoretical knowledge, but with practical skills that add real value to your professional toolkit. Overall, if you're eager to embrace the future of supply chain management and machine learning, this course is an essential stepping stone toward your goals!

Syllabus

Introduction to Programming Concepts and Python Practices

Welcome to the course! In this first module, we’ll learn about the fundamentals of programming and Python. We’ll start with basic data structures, functions, and loops and then some time becoming familiar with importing modules and libraries. Finally, we'll put our new skills to the test by optimizing a supply constraint problem using linear programming techniques.

In this next module, we'll dive into the most common tools used for data science: Python, and Numpy. We'll start with Numpy, getting used to np arrays and their main functionality. After getting familiar with loading in data of all types, we'll learn about some basic data description and cleaning techniques. We'll also learn to work with indexes and columns in Dataframes. We'll end with an introduction to plotting and summary statistics. We will use common supply chain data sets for our explorations

In this third module, we'll take our Pandas and Numpy skills to the next level, learning how to effectively combine and reshape data. We'll learn how to reshape data to fit with our needs through merges and pivots. This setup will help us tackle common data preprocessing steps necessary to run machine learning algorithms, such as one-hot encoding. Finally, we'll encounter the most important tools in our Pandas arsenal (Groupby-Apply-Transform) and explore its transformative functionality.

In this final project, we'll take collection of various data sets involving warehouse capacities, product demand, and freight rates to optimize cost of producing and shipping products.