This course is part of the Machine Learning for Supply Chains Specialization

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Rajvir Dua +1 more instructor

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Machine Learning for Supply Chains SpecializationLearnQuest

About this Course

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This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Course 1 of 4 in the

Machine Learning for Supply Chains Specialization

Beginner Level

No background required, though some general knowledge of supply chain will be helpful.

Approx. 13 hours to complete

English

What you will 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

Skills you will gain

Data Science
Pandas
Numpy
Supply Chain
Linear Programming (LP)

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 4 in the

Machine Learning for Supply Chains Specialization

Beginner Level

No background required, though some general knowledge of supply chain will be helpful.

Approx. 13 hours to complete

English

Instructors

Instructor rating

4.28/5 (8 Ratings)

Rajvir Dua

Head of Data Science, Machine Learning Solutions

7,786 Learners

8 Courses

Neelesh Tiruviluamala

Co-founder and CEO, Machine Learning Solutions

7,786 Learners

8 Courses

Offered by

LearnQuest

LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.

Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Introduction to Programming Concepts and Python Practices

4 hours to complete

12 videos (Total 45 min), 5 readings, 7 quizzes

12 videos

Welcome to the Course!56sWhy Python? Why Jupyter? Why ML?57sSetting Up the Environment2mModule Introduction57sPython and Jupyter Notebook Basics4mLists2mDictionaries3mLoops5mFunctions6mLibraries and Modules4mLinear Programming with Pulp (I)5mLinear Programming with Pulp (II)8m

5 readings

Jupyter Notebook Basics15mPython Docs: Data Structures15mKeyword Arguments10mTop 10 Python Libraries for Data Science10mWhat is PuLP?10m

3 practice exercises

Data Structures Practice Quiz5mUsing Data Structures with Pulp10mIntroduction to Python50m

Week2

Week 2

3 hours to complete

Digging Into Data: Common Tools for Data Science

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

3 hours to complete

9 videos (Total 34 min), 3 readings, 4 quizzes

9 videos

Module Introduction40s Introduction to Pandas and Numpy (A Tale of Two Matrices)3mDeep Dive into Numpy (Part I)3mDeep Dive Into Numpy (Part II)2mDeep Dive Into Numpy (III)4mIntroduction to Pandas5mIndexing in Pandas (I)3mIndexing in Pandas (II)4mPandas Deep Dive4m

3 readings

Numpy Quickstart10mInputing Missing Data10m10 min to Pandas10m

3 practice exercises

Numpy Basics10mPandas10mNumpy and Pandas Quiz50m

Week3

Week 3

3 hours to complete

Higher Level Data Wrangling and Manipulation

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.

3 hours to complete

5 videos (Total 23 min), 3 readings, 2 quizzes

Week4

Week 4

3 hours to complete

Course 1 Final Project

3 hours to complete

2 videos (Total 8 min)

About the Machine Learning for Supply Chains Specialization

Frequently Asked Questions

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Fundamentals of Machine Learning for Supply Chain

About this Course

What you will learn

Skills you will gain