Structuring Machine Learning Projects from Coursera

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

What's inside

Syllabus

ML Strategy

Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.

Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores ML project leadership skills and knowledge, which is standard industry practice

Builds practical diagnostic tools for ML project workflows, which is crucial for project success

Develops foundational intuition for splitting data and comparing ML strategies, which is a core skill for ML project leaders

Teaches end-to-end learning, transfer learning, and multi-task learning, which are key strategies for building effective ML systems

Provides practical tips for optimizing ML production workflows, which can save organizations time and resources

Taught by Andrew Ng, who is widely recognized for his pioneering work in ML

Reviews summary

Ml project architect

learners say this course is a must-have for practitioners and researchers due to its practical insights on managing and structuring machine learning projects, enabling them to improve their projects and allocate time and resources more effectively.

Introduces transfer learning, multi-task learning, and end-to-end learning, providing strategies for leveraging existing knowledge and data to improve model performance.

"This course has given me insights into the importance of choosing a better ML pipeline."

"Not only knowledge of ML is important."

"Thanks to Coursera, if I would have taken this class in school I must have missed this gemstone information."

Teaches strategies for prioritizing tasks, optimizing time and resources, and measuring success in machine learning projects, helping learners plan and execute projects more effectively.

"Don't skip this course! This in many ways is one of the best and most important courses in this specialisation."

"There's lots of great advice here which is relevant to any application of deep learning."

"As usual Prof. Ng brings his gentle and thoughtful manner to bear on some important topics."

Provides techniques for analyzing errors in models, such as human-level error, training error, training-dev error, dev error, and test error, enabling learners to analyze errors and make informed decisions.

"The lectures are arranged in a concise manner with only the necessary details."

"This is a shorter course than the other courses, but I learned a lot about different strategies of ML."

"The case study is also of top-notch which helped me to introduce to the application and various aspects of DL."

Explains the concepts of bias and variance in machine learning models, enabling learners to understand the trade-offs between model complexity and generalization performance.

"This short course focuses primarily on non-technical aspects of deep learning projects."

"The value of this subject matter is the focus on aspects that can make or break the success of a machine learning project."

"Given the fact that as much as 80% of deep learning efforts never make it "into production" (Gartner et al) spending time on these issues is highly recommended."

Emphasizes the importance of conducting error analysis and using simulations and case studies to practice strategies and concepts learned in the course, providing hands-on experience in managing machine learning projects.

"The course really streamlines and puts forth a structured approach to go for delivering a machine learning solution to a problem."

"It helps to complete my project in 2-3 months instead of a year that sometimes some of my colleagues take."

"They need to look at this course."

Career center

Learners who complete Structuring Machine Learning Projects will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

As a Machine Learning Engineer, you will design and implement machine learning models to solve real-world problems. You will need to have a strong understanding of ML algorithms, as well as the ability to apply them to real-world data. This course will teach you how to structure ML projects, which is an essential skill for any ML Engineer. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Data Scientist

As a Data Scientist, you will use ML and other data analysis techniques to uncover insights from data. You will need to have a strong understanding of ML algorithms, as well as the ability to apply them to real-world data. This course will teach you how to structure ML projects, which is an essential skill for any Data Scientist. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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AI Engineer

As an AI Engineer, you will design and implement AI systems. You will need to have a strong understanding of ML algorithms, as well as the ability to apply them to real-world data. This course will teach you how to structure ML projects, which is an essential skill for any AI Engineer. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Software Engineer

As a Software Engineer, you will design and implement software systems. You will need to have a strong understanding of software engineering principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Software Engineer who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Product Manager

As a Product Manager, you will be responsible for the development and launch of new products. You will need to have a strong understanding of product management principles, as well as the ability to apply them to real-world products. This course will teach you how to structure ML projects, which is a useful skill for any Product Manager who is working on ML products. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Business Analyst

As a Business Analyst, you will use data to help businesses make better decisions. You will need to have a strong understanding of data analysis techniques, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Business Analyst who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Data Analyst

As a Data Analyst, you will use data to help businesses make better decisions. You will need to have a strong understanding of data analysis techniques, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Data Analyst who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Statistician

As a Statistician, you will use statistical methods to analyze data and draw conclusions. You will need to have a strong understanding of statistical principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Statistician who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Operations Research Analyst

As an Operations Research Analyst, you will use mathematical models to help businesses make better decisions. You will need to have a strong understanding of operations research principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Operations Research Analyst who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Quantitative Analyst

As a Quantitative Analyst, you will use mathematical and statistical methods to analyze financial data. You will need to have a strong understanding of financial principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Quantitative Analyst who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Market Researcher

As a Market Researcher, you will use data to help businesses make better decisions about their products and services. You will need to have a strong understanding of market research principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Market Researcher who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Financial Analyst

As a Financial Analyst, you will use financial data to help businesses make better decisions about their investments. You will need to have a strong understanding of financial principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Financial Analyst who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Consultant

As a Consultant, you will help businesses solve problems and improve their performance. You will need to have a strong understanding of business principles, as well as the ability to apply them to real-world problems. This course will teach you how to structure ML projects, which is a useful skill for any Consultant who is working on ML projects. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Teacher

As a Teacher, you will teach students about a variety of subjects. You will need to have a strong understanding of the subject matter, as well as the ability to communicate it effectively to students. This course will teach you how to structure ML projects, which is a useful skill for any Teacher who is teaching ML. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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Writer

As a Writer, you will create written content for a variety of purposes. You will need to have a strong understanding of writing principles, as well as the ability to communicate effectively in writing. This course may be helpful for Writers who are interested in writing about ML. It will teach you how to structure ML projects, which is a useful skill for any Writer who is writing about ML. It will also teach you how to diagnose and fix errors in ML systems, which is another important skill for this role.

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