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Jon Reifschneider
This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.
Read more
This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.
Read more
This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.
At the conclusion of this course, you should be able to:
1) Identify opportunities to apply ML to solve problems for users
2) Apply the data science process to organize ML projects
3) Evaluate the key technology decisions to make in ML system design
4) Lead ML projects from ideation through production using best practices
Register for this course and see more details by visiting:
OpenCourser.com/course/i2mcec/managing
What's inside
Syllabus
Identifying Opportunities for Machine Learning
In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to heuristics.
Read more
Syllabus
Identifying Opportunities for Machine Learning
In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to heuristics.
Read more
Organizing ML Projects
In this module we will focus on the CRISP-DM data science process and how it can be used to organize ML projects. We will begin by understanding what is unique about ML project relative to normal software projects, and then discuss approaches to manage the inherent risks of ML projects. We will also walk through the key roles on a ML project team and how to organize work.
Data Considerations
In this module we will explore the key data-related issues that arise in ML projects. Data is the foundation of successful machine learning, and gathering data of sufficient quantity and quality with the right set of attributes is the key to a successful project. We will discuss the key considerations in sourcing data, cleaning data, and developing and selecting a feature set to use in modeling. The module will conclude with a discussion on best practices to ensure reproducibility of your data pipeline.
ML System Design & Technology Selection
In this module we will discuss the key decisions to make in designing ML systems, such as cloud vs. edge and online vs. batch, and compare the benefits of each type of system. We will then discuss the primary technology decisions to make in a ML project and introduce the common tools and technologies used to build ML models.
Model Lifecycle Management
The final module in the course focuses on identifying and mitigating the key issues which ML models experience once they are in production. We will discuss how to set up a robust ML system monitoring capability and define a model maintenance plan to maintain high performance of a production model. We will conclude with a discussion on the importance of versioning in ML systems to facilitate continued rapid iteration even after deployment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces tools and methods that are at the forefront of industry practices
Taught by a recognized expert from a prominent educational institution
Covers essential concepts and techniques for managing ML projects
Provides hands-on exercises and case studies for practical application
May require familiarity with data analysis and programming concepts
Save this course
Save Managing Machine Learning Projects to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Managing Machine Learning Projects with these
activities:
Review data science and machine learning concepts
Show steps
Start the course with a refreshed understanding of fundamental data science and machine learning concepts.
Browse courses on
Data Science
Show steps
-
Review introductory textbooks or online resources on data science and machine learning.
-
Complete practice problems or exercises to reinforce your understanding.
Refresh Probability Theory
Show steps
Understanding probability theory will improve your ability to understand the theory behind the machine learning models you will learn in the course.
Browse courses on
Probability Theory
Show steps
-
Review the basics of probability distributions: binomial, normal, Poisson
-
Review concepts of Bayesian statistics: Bayes' theorem, conjugate priors
-
Practice solving probability problems involving conditional probability and Bayes' theorem
Connect with experienced machine learning practitioners
Show steps
Seek guidance and advice from experts in the field to accelerate your learning.
Browse courses on
Mentorship
Show steps
-
Attend industry events and meetups to network with machine learning professionals.
-
Reach out to individuals whose work or experience aligns with your interests.
-
Clearly articulate your goals and how their mentorship can support your growth.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Practice Machine Learning Algorithms
Show steps
You will need to write code throughout this course. This practice will get you ready.
Browse courses on
Machine Learning Algorithms
Show steps
-
Implement linear regression from scratch in Python
-
Implement logistic regression from scratch
-
Implement a decision tree from scratch
-
Implement a k-means clustering algorithm from scratch
-
Implement a neural network from scratch
Solve machine learning coding challenges
Show steps
Sharpen your machine learning coding skills and problem-solving abilities.
Browse courses on
Python
Show steps
-
Identify reputable online platforms or resources for machine learning coding challenges.
-
Select problems that align with course topics and your skill level.
-
Allocate dedicated time for practicing and solving these challenges.
Attend machine learning workshops or conferences
Show steps
Expand your knowledge and network by attending industry events focused on machine learning.
Show steps
-
Identify reputable machine learning workshops or conferences that align with your interests and learning goals.
-
Register and actively participate in the sessions, taking notes and engaging in discussions.
-
Network with other attendees and speakers to exchange ideas and insights.
Follow online tutorials on machine learning best practices
Show steps
Enhance your machine learning development skills by learning industry best practices.
Show steps
-
Explore reputable online platforms or resources for machine learning tutorials.
-
Select tutorials that cover specific best practices or techniques relevant to the course.
-
Follow the tutorials diligently, taking notes and practicing the concepts.
Develop a machine learning deployment plan
Show steps
Gain practical experience in planning and executing the deployment of machine learning models.
Browse courses on
Model Deployment
Show steps
-
Choose a specific machine learning model and use case for your deployment plan.
-
Research and select appropriate tools and technologies for deployment.
-
Design and document a detailed deployment plan, including infrastructure, monitoring, and maintenance.
Develop a portfolio of machine learning projects
Show steps
Showcase your machine learning skills and understanding by creating a portfolio of practical projects.
Browse courses on
Machine Learning Projects
Show steps
-
Identify real-world problems or datasets that can be addressed using machine learning.
-
Design and implement machine learning models to solve these problems.
-
Write detailed documentation and present your results effectively.
Participate in machine learning competitions
Show steps
Apply your machine learning knowledge and skills in a competitive environment.
Browse courses on
Kaggle
Show steps
-
Identify relevant machine learning competitions that align with your interests and skill level.
-
Carefully read and understand the competition rules and guidelines.
-
Form a team or work individually to develop and submit your solutions.
-
Analyze your results and learn from the feedback and insights gained.
Review data science and machine learning concepts
Show steps
Start the course with a refreshed understanding of fundamental data science and machine learning concepts.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Review introductory textbooks or online resources on data science and machine learning.
- Complete practice problems or exercises to reinforce your understanding.
Refresh Probability Theory
Show steps
Understanding probability theory will improve your ability to understand the theory behind the machine learning models you will learn in the course.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Review the basics of probability distributions: binomial, normal, Poisson
- Review concepts of Bayesian statistics: Bayes' theorem, conjugate priors
- Practice solving probability problems involving conditional probability and Bayes' theorem
Connect with experienced machine learning practitioners
Show steps
Seek guidance and advice from experts in the field to accelerate your learning.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Attend industry events and meetups to network with machine learning professionals.
- Reach out to individuals whose work or experience aligns with your interests.
- Clearly articulate your goals and how their mentorship can support your growth.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Practice Machine Learning Algorithms
Show steps
You will need to write code throughout this course. This practice will get you ready.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Implement linear regression from scratch in Python
- Implement logistic regression from scratch
- Implement a decision tree from scratch
- Implement a k-means clustering algorithm from scratch
- Implement a neural network from scratch
Solve machine learning coding challenges
Show steps
Sharpen your machine learning coding skills and problem-solving abilities.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify reputable online platforms or resources for machine learning coding challenges.
- Select problems that align with course topics and your skill level.
- Allocate dedicated time for practicing and solving these challenges.
Attend machine learning workshops or conferences
Show steps
Expand your knowledge and network by attending industry events focused on machine learning.
Show steps
Show steps
Show steps
- Identify reputable machine learning workshops or conferences that align with your interests and learning goals.
- Register and actively participate in the sessions, taking notes and engaging in discussions.
- Network with other attendees and speakers to exchange ideas and insights.
Follow online tutorials on machine learning best practices
Show steps
Enhance your machine learning development skills by learning industry best practices.
Show steps
Show steps
Show steps
- Explore reputable online platforms or resources for machine learning tutorials.
- Select tutorials that cover specific best practices or techniques relevant to the course.
- Follow the tutorials diligently, taking notes and practicing the concepts.
Develop a machine learning deployment plan
Show steps
Gain practical experience in planning and executing the deployment of machine learning models.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Choose a specific machine learning model and use case for your deployment plan.
- Research and select appropriate tools and technologies for deployment.
- Design and document a detailed deployment plan, including infrastructure, monitoring, and maintenance.
Develop a portfolio of machine learning projects
Show steps
Showcase your machine learning skills and understanding by creating a portfolio of practical projects.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify real-world problems or datasets that can be addressed using machine learning.
- Design and implement machine learning models to solve these problems.
- Write detailed documentation and present your results effectively.
Participate in machine learning competitions
Show steps
Apply your machine learning knowledge and skills in a competitive environment.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify relevant machine learning competitions that align with your interests and skill level.
- Carefully read and understand the competition rules and guidelines.
- Form a team or work individually to develop and submit your solutions.
- Analyze your results and learn from the feedback and insights gained.
Career center
Learners who complete Managing Machine Learning Projects will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and maintaining machine learning models. They work closely with Data Scientists to develop and implement solutions to business problems. This course provides a deep understanding of the machine learning system design process, which is essential for success in this role.
Data Scientist
Data Scientist
Data Scientists use machine learning to solve a variety of business problems. They may work on projects such as developing new products, improving customer service, or reducing costs. This course provides a strong foundation in the machine learning process, which will be essential for success in this role.
Machine Learning Consultant
Machine Learning Consultant
Machine Learning Consultants provide machine learning services to clients. They work with clients to identify and solve business problems using machine learning techniques. This course provides a strong foundation in the machine learning consulting process, which is essential for success in this role.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They work with stakeholders to develop and implement solutions to business problems. This course provides a strong foundation in the data science process, which is essential for success in this role.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. They work with stakeholders to develop and implement solutions to data-related problems. This course provides a strong foundation in the data engineering process, which is essential for success in this role.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with stakeholders to develop and implement solutions to business problems. This course provides a strong foundation in the machine learning research process, which is essential for success in this role.
AI Architect
AI Architect
AI Architects are responsible for designing and implementing AI solutions. They work with stakeholders to develop and implement solutions to business problems. This course provides a strong foundation in the AI architecture process, which is essential for success in this role.
Data Science Consultant
Data Science Consultant
Data Science Consultants provide data science services to clients. They work with clients to identify and solve business problems using data science techniques. This course provides a strong foundation in the data science consulting process, which is essential for success in this role.
Agile Coach
Agile Coach
Agile Coaches help organizations adopt and implement agile practices. They work with teams to define and manage agile processes, and remove roadblocks. This course provides a strong foundation in agile principles and practices, which is essential for success in this role.
Product Owner
Product Owner
Product Owners are responsible for defining and managing the development of new products. They work with stakeholders to define product requirements, develop marketing strategies, and manage product development. This course provides a strong foundation in the product development process, which is essential for success in this role.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to identify and solve business problems. They may develop and implement solutions such as new products, processes, or systems. This course provides a strong foundation in the business analysis process, which is essential for success in this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may work on projects such as developing new products, improving customer service, or reducing costs. This course provides a solid understanding of the software development process, which is essential for success in this role.
Scrum Master
Scrum Master
Scrum Masters are responsible for facilitating agile development teams. They work with teams to define and manage sprints, and remove roadblocks. This course provides a strong foundation in the scrum process, which is essential for success in this role.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with stakeholders to define product requirements, develop marketing strategies, and manage product development. This course provides a strong foundation in the product development process, which is essential for success in this role.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, develop project plans, and manage project resources. This course provides a strong foundation in the project management process, which is essential for success in this role.
For more career information including salaries, visit:
OpenCourser.com/course/i2mcec/managing
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Managing Machine Learning Projects.
Practical guide to machine learning using Python. It covers the essential concepts of machine learning, as well as how to use popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow.
Save
Comprehensive guide to deep learning, covering the theoretical and practical aspects of the field. It valuable resource for anyone who wants to learn more about deep learning, regardless of their background.
Provides a comprehensive overview of data science, covering the essential concepts, techniques, and tools of the field. It valuable resource for anyone who wants to learn more about data science, regardless of their background.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers the essential concepts, techniques, and algorithms of the field, and valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of statistical learning, covering the essential concepts, techniques, and algorithms of the field. It valuable resource for anyone who wants to learn more about statistical learning, regardless of their background.
Provides a practical guide to machine learning for programmers. It covers the essential concepts, techniques, and tools of the field, and valuable resource for anyone who wants to learn more about machine learning.
Save
Provides a comprehensive overview of data mining, covering the essential concepts, techniques, and algorithms of the field. It valuable resource for anyone who wants to learn more about data mining, regardless of their background.
Provides a practical guide to machine learning using R. It covers the essential concepts, techniques, and tools of the field, and valuable resource for anyone who wants to learn more about machine learning.
Provides a practical guide to machine learning using Python. It covers the essential concepts, techniques, and tools of the field, and valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of deep learning for natural language processing. It covers the essential concepts, techniques, and algorithms of the field, and valuable resource for anyone who wants to learn more about deep learning for natural language processing.
Provides a comprehensive overview of reinforcement learning. It covers the essential concepts, techniques, and algorithms of the field, and valuable resource for anyone who wants to learn more about reinforcement learning.
Save
Provides a comprehensive overview of generative adversarial networks. It covers the essential concepts, techniques, and algorithms of the field, and valuable resource for anyone who wants to learn more about generative adversarial networks.
Save
Provides a comprehensive overview of the history and future of machine learning. It valuable resource for anyone who wants to learn more about the field, and its potential impact on society.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i2mcec/managing
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Effort
5 weeks of study, 3-5 hours per week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructor
Jon Reifschneider
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
AI Product Management. It's comprised of three courses:
- Managing Machine Learning Projects (viewing)
- Machine Learning Foundations for Product Managers
- Human Factors in AI
Good to know
Introduces tools and methods that are at the forefront of industry practices
Taught by a recognized expert from a prominent educational institution
Covers essential concepts and techniques for managing ML projects
Provides hands-on exercises and case studies for practical application
May require familiarity with data analysis and programming concepts
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