This specialization is intended for students who wish to use machine language to analyze and predict product usage and other similar tasks. There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
Machine Learning for Supply Chains Specialization
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Machine Learning for Supply Chains Specialization
Use Machine Learning in the Supply Chain. You will learn to use machine language techniques to analyze and predict retail stock in the supply chain.
Instructors: Neelesh Tiruviluamala
3,695 already enrolled
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Get in-depth knowledge of a subject
3.7
(65 reviews)
Intermediate level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
3.7
(65 reviews)
Intermediate level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Skills you'll gain
- Exploratory Data Analysis
- Customer Demand Planning
- Data Wrangling
- Operations Research
- Applied Machine Learning
- Data Manipulation
- Machine Learning
- Predictive Modeling
- Inventory Management
- Supply Chain
- Feature Engineering
- Image Analysis
- Data Analysis
- Supervised Learning
- Trend Analysis
- Data Science
- Time Series Analysis and Forecasting
- Data Visualization
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
17 practice exercises
Advance your subject-matter expertise
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- Develop a deep understanding of key concepts
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Specialization - 4 course series
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
Skills you'll gain
Category: Python Programming
Category: Data Structures
Category: Data Manipulation
Category: NumPy
Category: Programming Principles
Category: Pandas (Python Package)
Category: Data Analysis
Category: Exploratory Data Analysis
Category: Data Science
Category: Applied Machine Learning
Category: Data Transformation
Category: Data Wrangling
Category: Computer Programming
Category: Plot (Graphics)
Category: Operations Research
Category: Data Cleansing
Category: Supply Chain
What you'll learn
Building ARIMA models in Python to make demand predictions
Developing the framework for more advanced neural netowrks (such as LSTMs) by understanding autocorrelation and autoregressive models.
Skills you'll gain
Category: Time Series Analysis and Forecasting
Category: Trend Analysis
Category: Regression Analysis
Category: Forecasting
Category: Machine Learning
Category: Statistical Analysis
Category: Predictive Modeling
Category: Pandas (Python Package)
Category: Supply Chain
Category: Predictive Analytics
Category: Python Programming
Category: Data Visualization
Category: Data Analysis
Category: Matplotlib
Category: Customer Demand Planning
What you'll learn
Skills you'll gain
Category: Artificial Neural Networks
Category: Image Analysis
Category: Computer Vision
Category: Performance Tuning
Category: Natural Language Processing
Category: Applied Machine Learning
Category: Statistical Modeling
Category: Customer Demand Planning
Category: Machine Learning
Category: Predictive Modeling
Category: Supervised Learning
Category: Deep Learning
Category: Classification And Regression Tree (CART)
Category: Random Forest Algorithm
Category: Anomaly Detection
Category: Supply Chain Management
Category: Supply Chain
Category: Forecasting
Category: Unsupervised Learning
What you'll learn
Calcualte safety stock using SARIMA predictions combined with manipulaitng lead times.
Skills you'll gain
Category: Time Series Analysis and Forecasting
Category: Predictive Modeling
Category: Predictive Analytics
Category: Exploratory Data Analysis
Category: Pandas (Python Package)
Category: Statistical Modeling
Category: Feature Engineering
Category: Demand Planning
Category: Data Transformation
Category: Inventory Management System
Category: Matplotlib
Category: Machine Learning
Category: Product Knowledge
Category: Data Manipulation
Category: Data Visualization
Category: Inventory Management
Category: Inventory Control
Category: Data Analysis
Category: Data Wrangling
Category: Forecasting
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Frequently asked questions
To complete all four courses, you will spend 3-5 hours per week for 13 weeks.
Machine learning is mathematical. There are no specific background requirements, but knowledge of algebra and calculus will be helpful, as well as a general understanding of supply chain concepts.
Yes, the courses build knowledge as they progress so we recommend taking them in order.
More questions
Financial aid available,