LearnQuest
Fundamentals of Machine Learning for Supply Chain
LearnQuest

Fundamentals of Machine Learning for Supply Chain

This course is part of Machine Learning for Supply Chains Specialization

Taught in English

Some content may not be translated

Rajvir Dua
Neelesh Tiruviluamala

Instructors: Rajvir Dua

2,954 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.8

(31 reviews)

Beginner level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn to merge, clean, and manipulate data using Python libraries such as Numpy and Pandas

  • Gain familiarity with the basic and advaned Python functonalities such as importing and using modules, list compreohensions, and lambda functions.

  • Solve a supply chain cost optimization problem using Linear Programming with Pulp

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.8

(31 reviews)

Beginner level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Machine Learning for Supply Chains Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

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.

What's included

12 videos5 readings3 quizzes4 programming assignments1 discussion prompt5 ungraded labs

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

What's included

9 videos3 readings3 quizzes1 programming assignment1 discussion prompt2 ungraded labs

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.

What's included

5 videos3 readings1 quiz1 programming assignment1 discussion prompt2 ungraded labs

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.

What's included

2 videos1 quiz1 programming assignment1 ungraded lab

Instructors

Instructor ratings
4.4 (10 ratings)
Rajvir Dua
LearnQuest
8 Courses12,452 learners

Offered by

LearnQuest

Recommended if you're interested in Data Analysis

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3.8

31 reviews

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Reviewed on Jan 24, 2024

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