DeepLearning.AI - Structuring Machine Learning Projects
5.0 /5- Offered byCoursera
Structuring Machine Learning Projects at Coursera Overview
Duration | 5 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Structuring Machine Learning Projects at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 5 in the Deep Learning Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
- Approx. 5 hours to complete
- English Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Structuring Machine Learning Projects at Coursera Course details
- 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.
Structuring Machine Learning Projects at Coursera Curriculum
ML Strategy (1)
Why ML Strategy
Orthogonalization
Single number evaluation metric
Satisficing and Optimizing metric
Train/dev/test distributions
Size of the dev and test sets
When to change dev/test sets and metrics
Why human-level performance?
Avoidable bias
Understanding human-level performance
Surpassing human-level performance
Improving your model performance
Andrej Karpathy interview
Machine Learning flight simulator
Bird recognition in the city of Peacetopia (case study)
ML Strategy (2)
Carrying out error analysis
Cleaning up incorrectly labeled data
Build your first system quickly, then iterate
Training and testing on different distributions
Bias and Variance with mismatched data distributions
Addressing data mismatch
Transfer learning
Multi-task learning
What is end-to-end deep learning?
Whether to use end-to-end deep learning
Ruslan Salakhutdinov interview
Autonomous driving (case study)
Structuring Machine Learning Projects at Coursera Admission Process
Important Dates
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Structuring Machine Learning Projects at Coursera Students Ratings & Reviews
3 Ratings- 4-53
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Data ScienceMachine LearningMachine Learning and Pattern RecognitionStructuring Machine Learning Projects