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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.
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.
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.
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
What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
Effective ml project strategy and structure
According to learners, Structuring Machine Learning Projects is a highly recommended course that provides invaluable practical guidance for anyone involved in leading or managing ML initiatives. Students consistently praise the instructor, Andrew Ng, for his ability to explain complex strategic concepts with clarity, making the content immediately applicable to real-world scenarios. Key takeaways frequently mentioned include effective approaches to error analysis, setting up development and test sets correctly, and understanding advanced techniques like transfer learning from a strategic viewpoint. While the course is lauded for its strategic depth, some reviewers note its focus is on high-level concepts rather than low-level coding details, suggesting it is best suited for managers and technical leads or those with some prior ML exposure.
High-level strategic focus, less coding.
"I found this course too abstract."
"If you're looking for implementation details or coding exercises, look elsewhere."
"I expected more technical depth. It's very high-level strategy."
"Needed more concrete examples and code. The 'strategy' part felt like common sense sometimes."
Fits well with other DL courses.
"It complements the previous courses well."
"A must-take if you are following the Deep Learning Specialization."
"Helps solidify concepts from previous courses by providing context."
"This course adds a crucial strategic layer to the technical skills learned earlier."
Best for managers, leads, or strategists.
"Highly recommend for working professionals."
"This course provides the 'industry experience' that you might otherwise get only after years of ML work experience."
"Very valuable course for understanding the ML project lifecycle from a strategic perspective."
"It's theory grounded in practice, suitable for strategic roles."
Complex concepts explained simply and clearly.
"Andrew Ng explains complex topics like error analysis and data splits so clearly."
"The lectures were engaging and the concepts immediately applicable."
"The explanations of advanced topics like multi-task learning are good."
"Andrew Ng is a great instructor. The advice on error analysis is particularly useful."
Provides real-world strategy and framework.
"The practical advice on structuring projects and prioritizing work is gold for anyone in ML."
"This course changed how I approach ML projects. The framework for diagnosing errors and deciding what to work on next is incredibly helpful."
"This course offers unique perspectives on how to make ML projects succeed in the real world."
"I learned practical tools and strategies that I could apply immediately to my work."
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
Featured in The Course Notes
This course is mentioned in our blog,
The Course Notes. Read
one article that features
Structuring Machine Learning Projects:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
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
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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
Featured in The Course Notes
Read
one article that features
this course,
Structuring Machine Learning Projects
from our blog, The Course Notes:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
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