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Andrew Ng,
Kian Katanforoosh,
and
Younes Bensouda Mourri
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/qt4vss/structuring
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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
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
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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."
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 Structuring Machine Learning Projects with these
activities:
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of deep learning concepts, algorithms, and applications by reading this foundational text.
View
Deep Learning
on Amazon
Show steps
-
Read and understand the chapters on deep neural networks, convolutional neural networks, and recurrent neural networks.
-
Work through the exercises and examples provided in the book.
-
Discuss the key concepts with peers or mentors.
Attend a Machine Learning Meetup
Show steps
Connect with other machine learning enthusiasts and professionals to exchange knowledge and stay informed about industry trends.
Browse courses on
Networking
Show steps
-
Find a local machine learning meetup or conference.
-
Attend the event and actively participate in discussions.
-
Exchange contact information with potential mentors or collaborators.
Data Splitting Exercises
Show steps
Practice splitting data into training, validation, and test sets to gain a deeper understanding of how these sets impact model performance.
Browse courses on
Data Splitting
Show steps
-
Review the concepts of training, validation, and test sets.
-
Use a library or framework to split a dataset into these three sets.
-
Train a simple model on each set and compare the results.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Tutorials: Building and Training Deep Learning Models
Show steps
Follow guided tutorials to build and train deep learning models using TensorFlow, gaining practical experience.
Browse courses on
TensorFlow
Show steps
-
Create a TensorFlow environment and install the necessary libraries.
-
Work through the tutorials on building a simple neural network, training it on a dataset, and evaluating its performance.
-
Experiment with different model architectures and training parameters.
Study Group: Machine Learning Algorithms
Show steps
Engage in discussions and problem-solving with a study group to enhance your understanding of machine learning algorithms.
Show steps
-
Form a study group with peers who have similar interests in machine learning.
-
Choose a specific machine learning algorithm to focus on.
-
Discuss the algorithm's strengths, weaknesses, and applications.
-
Work together to solve problems and implement the algorithm using a programming language.
Error Analysis Report
Show steps
Conduct a thorough error analysis on a machine learning model to identify and address potential sources of errors.
Browse courses on
Error Analysis
Show steps
-
Choose a machine learning model and train it on a dataset.
-
Evaluate the model's performance on a held-out test set.
-
Identify potential sources of errors by analyzing the model's predictions and the data.
-
Propose and implement strategies to reduce the identified errors.
-
Re-evaluate the model's performance and document the improvements.
Contribute to an Open-Source Machine Learning Project
Show steps
Gain practical experience and contribute to the machine learning community by participating in an open-source project.
Browse courses on
Open Source
Show steps
-
Identify a suitable open-source machine learning project.
-
Review the project documentation and codebase.
-
Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit a pull request with your contributions.
-
Collaborate with other contributors and maintain your contributions over time.
Write a Blog Post on a Machine Learning Topic
Show steps
Solidify your understanding of machine learning concepts by writing and sharing a blog post on a topic of your interest.
Browse courses on
Content Creation
Show steps
-
Choose a specific machine learning topic that you are knowledgeable about.
-
Research and gather information from reputable sources.
-
Organize your ideas and write a well-structured blog post.
-
Publish your blog post on a platform such as Medium or LinkedIn.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of deep learning concepts, algorithms, and applications by reading this foundational text.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read and understand the chapters on deep neural networks, convolutional neural networks, and recurrent neural networks.
- Work through the exercises and examples provided in the book.
- Discuss the key concepts with peers or mentors.
Attend a Machine Learning Meetup
Show steps
Connect with other machine learning enthusiasts and professionals to exchange knowledge and stay informed about industry trends.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find a local machine learning meetup or conference.
- Attend the event and actively participate in discussions.
- Exchange contact information with potential mentors or collaborators.
Data Splitting Exercises
Show steps
Practice splitting data into training, validation, and test sets to gain a deeper understanding of how these sets impact model performance.
Browse courses on
Data Splitting
Show steps
Show steps
Show steps
- Review the concepts of training, validation, and test sets.
- Use a library or framework to split a dataset into these three sets.
- Train a simple model on each set and compare the results.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Tutorials: Building and Training Deep Learning Models
Show steps
Follow guided tutorials to build and train deep learning models using TensorFlow, gaining practical experience.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Create a TensorFlow environment and install the necessary libraries.
- Work through the tutorials on building a simple neural network, training it on a dataset, and evaluating its performance.
- Experiment with different model architectures and training parameters.
Study Group: Machine Learning Algorithms
Show steps
Engage in discussions and problem-solving with a study group to enhance your understanding of machine learning algorithms.
Show steps
Show steps
Show steps
- Form a study group with peers who have similar interests in machine learning.
- Choose a specific machine learning algorithm to focus on.
- Discuss the algorithm's strengths, weaknesses, and applications.
- Work together to solve problems and implement the algorithm using a programming language.
Error Analysis Report
Show steps
Conduct a thorough error analysis on a machine learning model to identify and address potential sources of errors.
Browse courses on
Error Analysis
Show steps
Show steps
Show steps
- Choose a machine learning model and train it on a dataset.
- Evaluate the model's performance on a held-out test set.
- Identify potential sources of errors by analyzing the model's predictions and the data.
- Propose and implement strategies to reduce the identified errors.
- Re-evaluate the model's performance and document the improvements.
Contribute to an Open-Source Machine Learning Project
Show steps
Gain practical experience and contribute to the machine learning community by participating in an open-source project.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify a suitable open-source machine learning project.
- Review the project documentation and codebase.
- Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit a pull request with your contributions.
- Collaborate with other contributors and maintain your contributions over time.
Write a Blog Post on a Machine Learning Topic
Show steps
Solidify your understanding of machine learning concepts by writing and sharing a blog post on a topic of your interest.
Browse courses on
Content Creation
Show steps
Show steps
Show steps
- Choose a specific machine learning topic that you are knowledgeable about.
- Research and gather information from reputable sources.
- Organize your ideas and write a well-structured blog post.
- Publish your blog post on a platform such as Medium or LinkedIn.
Career center
Learners who complete Structuring Machine Learning Projects will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
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.
Data Scientist
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.
AI Engineer
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.
Software Engineer
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.
Product Manager
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.
Business Analyst
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.
Data Analyst
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.
Statistician
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.
Operations Research Analyst
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.
Quantitative Analyst
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.
Market Researcher
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.
Financial Analyst
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.
Consultant
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.
Teacher
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.
Writer
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.
For more career information including salaries, visit:
OpenCourser.com/course/qt4vss/structuring
Reading list
We've selected 12 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
Structuring Machine Learning Projects.
Save
Provides a comprehensive overview of deep learning concepts and algorithms, with a focus on theoretical foundations.
Provides hands-on experience with machine learning libraries and tools, including Scikit-Learn, Keras, and TensorFlow.
Provides a practical guide to machine learning, with a focus on real-world applications.
Provides a comprehensive overview of machine learning concepts and algorithms, with a focus on theoretical foundations.
Provides a comprehensive overview of machine learning algorithms, with a focus on mathematical foundations.
Provides a comprehensive overview of statistical learning methods, with a focus on sparsity.
Provides a comprehensive overview of reinforcement learning concepts and algorithms, which are used in many machine learning applications.
Provides a comprehensive overview of natural language processing (NLP) techniques, with a focus on deep learning.
Save
Provides a comprehensive overview of computer vision algorithms and applications.
Provides a practical guide to deep learning using the Fastai and PyTorch libraries.
Provides a practical guide to machine learning, with a focus on hacking and security applications.
Provides a comprehensive overview of statistical learning methods, with a focus on practical applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qt4vss/structuring
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course
Level
Introductory
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
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اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Deep Learning. It's comprised of five courses:
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects (viewing)
- Convolutional Neural Networks
- Sequence Models
- Neural Networks and Deep Learning
Good to know
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
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