Managing ML Projects
This second course in Duke University's AI Product Management Specialization delves into the practical aspects of managing machine learning projects, such as the identification of opportunities, the application of data science processes, the making of critical technological decisions, and the implementation of best practices from concept to production.
Description for Managing ML Projects
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Duke University
Duration: 18 hours (approximately)
Schedule: Flexible
Pricing for Managing ML Projects
Use Cases for Managing ML Projects
FAQs for Managing ML Projects
Reviews for Managing ML Projects
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Managing ML Projects
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
The AI tool offers real-time behavior segmentation across industries, integrating diverse data sources and leveraging the Personalive� system for personalized insights, with resources available for data scientists.
Careerdekho AI assists users in discovering suitable careers through personalized recommendations across diverse fields, offering a free AI assessment and expert consultations for refined career planning.
The AI-powered decision support tool offers predictive analytics, data visualization, and seamless integration, aiding users in making informed decisions efficiently, though it may require some time to master its advanced features.
The AI tool provides comprehensive support for task management in Scrum and Kanban, offering efficient planning tools and multi-language support, although it may have a learning curve and limited integrations.
GitMind is a collaborative ideation platform offering various diagram creation tools, fostering brainstorming sessions and strategic planning with real-time collaboration, while providing a user-friendly interface and versatile diagram support, although it has limited offline functionality and lacks a dedicated mobile app.
The tool provides metadata management, data governance implementation, and consulting services, fostering a data-driven culture, but lacks detailed software descriptions, readily available pricing information, and sufficient user reviews.
The platform enhances workflow management and collaboration by integrating with popular applications, offering features like communication organization, task creation, and customizable summaries, albeit requiring a learning curve for novice users and occasional pending accessibility issues.
This tool optimizes customer-facing team meetings by automating meeting summaries, facilitating contextual linking, and integrating with standard tools, although users may require time to adapt, and overreliance on the tool could impact individual note-taking skills.
Orygo AI streamlines knowledge management by integrating with multiple applications, offering AI-powered search, tutorial creation, and personalized learning paths, although novice users may face initial challenges with feature overload and platform dependence.
Featured Tools
An overview of machine learning for business applications is provided in this course, which instructs participants on the development and utilization of ML models with BigQuery.
With practical experience in platform architecture and data querying, this course offers a basic understanding of data engineering, covering important ideas, tools, and career pathways.
This one-week intensive course offers fundamental knowledge of data science and big data, tailored for non-technical management.
Learn to develop, train, and assess neural networks using TensorFlow to resolve classification issues by understanding the fundamental principles of neural networks.
The course encompasses the fundamentals of supervised and unsupervised machine learning for financial data, as well as logistic regression, classification algorithms, investment management models, and practical implementation using Python.