Description for Fundamentals of ML for Supply Chain
Data Manipulation with Python: Teach yourself how to merge, sanitize, and manipulate datasets with Python libraries like Numpy and Pandas.
Advanced Python Functionalities: Become proficient in the application of lambda functions, the utilization of list comprehensions, and the importation of modules to facilitate efficient programming.
Exploratory Data Analysis (EDA): Create Pythonic skills and best practices for analyzing complex supply chain datasets, with techniques that are applicable to other disciplines.
Optimization of the Supply Chain: Utilize Linear Programming and the Pulp library to resolve a supply chain cost optimization issue.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by LearnQuest
Duration: 3 weeks at 4 hours a week
Schedule: Flexible
Pricing for Fundamentals of ML for Supply Chain
Use Cases for Fundamentals of ML for Supply Chain
FAQs for Fundamentals of ML for Supply Chain
Reviews for Fundamentals of ML for Supply Chain
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Fundamentals of ML for Supply Chain
NovaceneAI streamlines the organization of unstructured text data with AI algorithms, offering dedicated cloud hosting, adaptability across sectors, and robust data privacy measures.
Posit offers a comprehensive platform with enterprise solutions, cloud applications, community resources, and deployment solutions to enhance productivity in data science teams.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Learn to use generative AI tools to improve data science workflows, enhance datasets, and refine machine learning models through practical projects.
Gain a comprehensive understanding of AI's potential, ethical considerations, and applications in efficient programming and common coding tasks using various LLMs.
Master Python programming for software development and data science, including core logic, Jupyter Notebooks, libraries like NumPy and Pandas, and web data gathering with Beautiful Soup and APIs.
Understand AI, its applications, concepts, ethical concerns, and receive expert career guidance.
Gain a comprehensive understanding of AI terminology, applications, development, and strategy, while navigating ethical and societal considerations in a non-technical context.
Prepare for a vocation as a data scientist. Acquire hands-on experience and in-demand skills to become job-ready in as little as five months. No prior experience is necessary.
Learn to create and diversify portfolio strategies, apply machine learning to financial data, and utilize quantitative modeling and data analytics for investment decisions.
Featured Tools
Prepare yourself for your initial position in business intelligence. Develop the essential competencies required to initiate a career in business intelligence (BI) within two months. There is no prerequisite for a degree or prior experience.
This course offers practical information regarding the ethical implications, applications, and categories of machine learning.
The course offers business leaders critical insights into the ways in which AI and Machine Learning are revolutionizing industries and influencing strategic decision-making.
Examine the development and deployment of interactive Python data applications, with a particular emphasis on Recommender Systems and the use of Python web frameworks to deploy and monitor machine learning models.
Enables learners to efficiently create compelling, goal-oriented content across multiple platforms by utilizing Anyword's AI tools.