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Anna Koop
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
Read more
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
Read more
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).
This is the final course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute (Amii).
Register for this course and see more details by visiting:
OpenCourser.com/course/agiw9v/optimizing
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What's inside
Syllabus
Machine Learning Strategy
This week we'll present tools for understanding the overall strategy your business needs in order to see the best returns on ML investment. From understanding the current status to navigating ownership and setting up a team, this week is about understanding applied machine learning in a successful business context.
Read more
Syllabus
Machine Learning Strategy
This week we'll present tools for understanding the overall strategy your business needs in order to see the best returns on ML investment. From understanding the current status to navigating ownership and setting up a team, this week is about understanding applied machine learning in a successful business context.
Read more
Responsible Machine Learning
This week we'll talk about the broader context of machine learning: how as developers we have responsibilities regarding how our technology will be used. Using case studies and existing frameworks we'll give you the tools to figure out your own ethical approach to realize the best outcomes while deploying machine learning in the real world.
Machine Learning in Production & Planning
An important aspect of machine learning in the real world is considering how your machine learning models are integrated with existing systems, and what effect they have on your operations. This week we'll review things you should consider as you turn QuAMs and machine learning models into operational tools.
Care and Feeding of your Machine Learning System
Work doesn't end just because your model is deployed! In our final week we'll go over all the things you need to consider in the context of an actual working system.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in deploying, maintaining, and optimizing applied machine learning models in business context, which is highly relevant in industry
Prepares learners to navigate challenges and maximize benefits of applied machine learning in real-world business scenarios
Integrates ethical considerations into the deployment of machine learning models, ensuring responsible use of technology
Assumes prior knowledge in Python programming, linear algebra, and statistics, which may limit accessibility for beginners
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Reviews summary
Machine learning optimization course
Learners say that this Machine Learning Optimization course from Alberta Machine Intelligence Institute is a great introduction to Machine Learning with engaging assignments. Overall, this course is largely positive but there were many complaints about the peer-graded assignments and slow peer-grading affecting progress.
Course has interesting material.
"The course walks you through almost all possible scenarios that will need optimization."
Course serves as a good intro to ML.
"Great Introduction course to Machine Learning..."
"This last course has a great approach of the business applications."
Peer grading can be frustrating and slow learners down.
"Those peer graded tests are a waste of time."
"On the downside, Peer-graded Assignment block our progress on the course."
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 Optimizing Machine Learning Performance with these
activities:
Review fundamental concepts in statistics and probability
Show steps
Strengthen your foundation in statistics and probability to enhance your understanding of machine learning algorithms and models.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on basic statistics
-
Solve practice problems on probability distributions
Review essential linear algebra concepts by reading Hoffman and Kunze's 'Linear Algebra'
Show steps
Review the fundamentals of linear algebra to strengthen your understanding of vector spaces, matrices, and linear transformations, which are essential concepts for machine learning algorithms.
View
Linear Algebra
on Amazon
Show steps
-
Read Chapter 1: Vector Spaces
-
Solve practice problems on vector operations
-
Review Chapter 2: Linear Transformations
Solve LeetCode problems related to machine learning algorithms
Show steps
Sharpen your coding skills and deepen your understanding of machine learning algorithms by solving LeetCode problems specifically designed for machine learning.
Show steps
-
Create a LeetCode account
-
Start solving problems in the 'Machine Learning' category
-
Review solutions and discuss with peers
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or online forum dedicated to machine learning projects
Show steps
Connect with other learners and practitioners to share knowledge, collaborate on projects, and stay updated on the latest advancements in machine learning.
Show steps
-
Join a relevant online forum or community
-
Introduce yourself and participate in discussions
-
Share your own projects and seek feedback
Develop a machine learning roadmap for a specific business case
Show steps
Apply the concepts learned in the course to create a practical roadmap for implementing machine learning solutions in a specific business context.
Browse courses on
Business Strategy
Show steps
-
Identify a business case and define project goals
-
Research and select appropriate machine learning algorithms
-
Develop a plan for data collection and preparation
-
Create a deployment strategy
Write a blog post or article on a specific machine learning technique
Show steps
Solidify your understanding of a particular machine learning technique by explaining it clearly in a written format.
Browse courses on
Technical Writing
Show steps
-
Choose a specific machine learning technique to focus on
-
Research and gather relevant information
-
Organize your content and create an outline
-
Write and edit your blog post or article
Complete the 'Machine Learning Engineering for Production' specialization on Coursera
Show steps
Gain practical experience in deploying and maintaining machine learning models in real-world production environments.
Browse courses on
Machine Learning Engineering
Show steps
-
Enroll in the specialization
-
Complete the 'MLOps: Continuous Delivery and Automation' course
-
Complete the 'MLOps: Model Monitoring and Validation' course
Review fundamental concepts in statistics and probability
Show steps
Strengthen your foundation in statistics and probability to enhance your understanding of machine learning algorithms and models.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on basic statistics
- Solve practice problems on probability distributions
Review essential linear algebra concepts by reading Hoffman and Kunze's 'Linear Algebra'
Show steps
Review the fundamentals of linear algebra to strengthen your understanding of vector spaces, matrices, and linear transformations, which are essential concepts for machine learning algorithms.
View
Linear Algebra
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Vector Spaces
- Solve practice problems on vector operations
- Review Chapter 2: Linear Transformations
Solve LeetCode problems related to machine learning algorithms
Show steps
Sharpen your coding skills and deepen your understanding of machine learning algorithms by solving LeetCode problems specifically designed for machine learning.
Show steps
Show steps
Show steps
- Create a LeetCode account
- Start solving problems in the 'Machine Learning' category
- Review solutions and discuss with peers
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or online forum dedicated to machine learning projects
Show steps
Connect with other learners and practitioners to share knowledge, collaborate on projects, and stay updated on the latest advancements in machine learning.
Show steps
Show steps
Show steps
- Join a relevant online forum or community
- Introduce yourself and participate in discussions
- Share your own projects and seek feedback
Develop a machine learning roadmap for a specific business case
Show steps
Apply the concepts learned in the course to create a practical roadmap for implementing machine learning solutions in a specific business context.
Browse courses on
Business Strategy
Show steps
Show steps
Show steps
- Identify a business case and define project goals
- Research and select appropriate machine learning algorithms
- Develop a plan for data collection and preparation
- Create a deployment strategy
Write a blog post or article on a specific machine learning technique
Show steps
Solidify your understanding of a particular machine learning technique by explaining it clearly in a written format.
Browse courses on
Technical Writing
Show steps
Show steps
Show steps
- Choose a specific machine learning technique to focus on
- Research and gather relevant information
- Organize your content and create an outline
- Write and edit your blog post or article
Complete the 'Machine Learning Engineering for Production' specialization on Coursera
Show steps
Gain practical experience in deploying and maintaining machine learning models in real-world production environments.
Browse courses on
Machine Learning Engineering
Show steps
Show steps
Show steps
- Enroll in the specialization
- Complete the 'MLOps: Continuous Delivery and Automation' course
- Complete the 'MLOps: Model Monitoring and Validation' course
Career center
Learners who complete Optimizing Machine Learning Performance will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, deploy, and maintain machine learning models. This course is a perfect fit for aspiring Machine Learning Engineers because it teaches the skills and knowledge necessary to succeed in this role. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to launch a successful career as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. This course is a great fit for aspiring Data Scientists because it provides a strong foundation in machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to launch a successful career as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be helpful for aspiring Software Engineers who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to develop software systems that incorporate machine learning.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be helpful for aspiring Product Managers who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to develop and launch products that incorporate machine learning.
Business Analyst
Business Analyst
Business Analysts analyze business data to identify opportunities for improvement. This course may be helpful for aspiring Business Analysts who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to analyze business data and identify opportunities for improvement using machine learning.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course may be helpful for aspiring Data Analysts who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to collect, clean, and analyze data using machine learning.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions. This course may be helpful for aspiring Statisticians who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to collect, analyze, and interpret data using machine learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency of operations. This course may be helpful for aspiring Operations Research Analysts who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to improve the efficiency of operations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may be helpful for aspiring Quantitative Analysts who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to analyze financial data.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be helpful for aspiring Actuaries who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to assess risk and uncertainty.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to an organization. This course may be helpful for aspiring Risk Managers who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to identify and assess risks to an organization.
Compliance Officer
Compliance Officer
Compliance Officers ensure that an organization complies with laws and regulations. This course may be helpful for aspiring Compliance Officers who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to ensure that an organization complies with laws and regulations.
Auditor
Auditor
Auditors examine financial records to ensure that they are accurate and complete. This course may be helpful for aspiring Auditors who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to examine financial records.
Tax Accountant
Tax Accountant
Tax Accountants prepare and file tax returns for individuals and businesses. This course may be helpful for aspiring Tax Accountants who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to prepare and file tax returns.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course may be helpful for aspiring Financial Analysts who want to learn more about machine learning. The course covers a wide range of topics, including machine learning strategy, responsible machine learning, machine learning in production, and the care and feeding of machine learning systems. By taking this course, you will gain the skills and knowledge you need to use machine learning to analyze financial data and make investment recommendations.
For more career information including salaries, visit:
OpenCourser.com/course/agiw9v/optimizing
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
Optimizing Machine Learning Performance.
Provides a comprehensive overview of statistical methods for machine learning. It great resource for learners who want to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of data mining, covering the fundamental concepts and algorithms. It great resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It great resource for learners who want to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts and algorithms. It great resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of statistical learning, covering the fundamental concepts and algorithms. It great resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of pattern recognition and machine learning, covering the fundamental concepts and algorithms. It great resource for learners who want to gain a deeper understanding of the field.
Provides a practical guide to machine learning using R. It covers the fundamental concepts and algorithms, as well as how to apply machine learning to real-world problems.
Provides a practical guide to machine learning using Python. It covers the fundamental concepts and algorithms, as well as how to apply machine learning to real-world problems.
Save
Provides a practical guide to predictive modeling using R. It covers the fundamental concepts and algorithms, as well as how to apply predictive modeling to real-world problems.
Provides a practical guide to machine learning for hackers. It covers the fundamental concepts and algorithms, as well as how to apply machine learning to real-world problems.
Provides a practical guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It great resource for learners who want to apply machine learning techniques to real-world problems.
Save
Provides a practical guide to machine learning for business professionals. It covers the fundamental concepts and algorithms, as well as how to apply machine learning to real-world business problems.
Save
Provides a gentle introduction to machine learning for beginners. It covers the fundamental concepts and algorithms, as well as how to apply machine learning to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/agiw9v/optimizing
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Alberta Machine Intelligence Institute
Instructor
Anna Koop
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This course belongs to a
collection
called
Machine Learning: Algorithms in the Real World. It's comprised of four courses:
- Data for Machine Learning
- Machine Learning Algorithms: Supervised Learning Tip to Tail
- Introduction to Applied Machine Learning
- Optimizing Machine Learning Performance (viewing)
Good to know
Develops skills in deploying, maintaining, and optimizing applied machine learning models in business context, which is highly relevant in industry
Prepares learners to navigate challenges and maximize benefits of applied machine learning in real-world business scenarios
Integrates ethical considerations into the deployment of machine learning models, ensuring responsible use of technology
Assumes prior knowledge in Python programming, linear algebra, and statistics, which may limit accessibility for beginners
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