Structuring Machine Learning Projects

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Structuring Machine Learning Projects

This course is part of Deep Learning Specialization

Instructors: Andrew Ng +2 more

Top Instructor

513,578 already enrolled

2 modules

Gain insight into a topic and learn the fundamentals.

50,137 reviews

Beginner level

Recommended experience

Flexible schedule

7 hours to complete

Learn at your own pace

97%

Most learners liked this course

2 modules

Gain insight into a topic and learn the fundamentals.

50,137 reviews

Beginner level

Recommended experience

Flexible schedule

7 hours to complete

Learn at your own pace

97%

Most learners liked this course

Skills you'll gain

Category: Model Evaluation
Category: AI Product Strategy
Category: Applied Machine Learning
Category: Transfer Learning
Category: Deep Learning
Category: MLOps (Machine Learning Operations)
Category: Data-Driven Decision-Making
Category: Debugging
Category: Machine Learning
Category: Performance Tuning

Details to know

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Assessments

2 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

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.

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

What's included

13 videos3 readings1 assignment

13 videos Total 100 minutes

Why ML Strategy 3 minutes
Orthogonalization 11 minutes
Single Number Evaluation Metric 7 minutes
Satisficing and Optimizing Metric 6 minutes
Train/Dev/Test Distributions 7 minutes
Size of the Dev and Test Sets 6 minutes
When to Change Dev/Test Sets and Metrics? 11 minutes
Why Human-level Performance? 6 minutes
Avoidable Bias 7 minutes
Understanding Human-level Performance 11 minutes
Surpassing Human-level Performance 6 minutes
Improving your Model Performance 5 minutes
Andrej Karpathy Interview 15 minutes

3 readings Total 5 minutes

Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! 2 minutes
Lecture Notes W1 1 minute
Machine Learning Flight Simulator (Introduction to the Quizzes) 2 minutes

1 assignment Total 75 minutes

Bird Recognition in the City of Peacetopia (Quiz Case Study) 75 minutes

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.

What's included

11 videos2 readings1 assignment

11 videos Total 132 minutes

Carrying Out Error Analysis 11 minutes
Cleaning Up Incorrectly Labeled Data 13 minutes
Build your First System Quickly, then Iterate 5 minutes
Training and Testing on Different Distributions 11 minutes
Bias and Variance with Mismatched Data Distributions 18 minutes
Addressing Data Mismatch 10 minutes
Transfer Learning 11 minutes
Multi-task Learning 13 minutes
What is End-to-end Deep Learning? 12 minutes
Whether to use End-to-end Deep Learning 10 minutes
Ruslan Salakhutdinov Interview 17 minutes

2 readings Total 11 minutes

Lecture Notes W2 1 minute
Acknowledgments 10 minutes

1 assignment Total 75 minutes

Autonomous Driving (Quiz Case Study) 75 minutes

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Instructors

Instructor ratings

(2,890 ratings)

Top Instructor

Andrew Ng

DeepLearning.AI

51 Courses 9,611,314 learners

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DeepLearning.AI

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Reviewed on Mar 30, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

Reviewed on Feb 24, 2020

Generally, the course is great. This is a short course and could be combined with other courses in this series. Also, some knowledge such as data splitting has been introduced in the courses before.

Reviewed on Mar 18, 2019

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.