About this course: You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.
Who is this class for: Pre-requisites: - This course is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and prioritization for their work. - It is recommended that you take course one and two of this specialization (Neural Networks and Deep Learning, and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization) prior to beginning this course.
Taught by: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Taught by: Teaching Assistant - Kian Katanforoosh, M.S. Stanford University (Walter J. Gores 2017), B.S Ecole Centrale Paris
Taught by: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University
| Basic Info | Course 3 of 5 in the Deep Learning Specialization |
| Level | Beginner |
| Commitment | 2 weeks of study, 3-4 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Why ML Strategy
- Video: Orthogonalization
- Video: Single number evaluation metric
- Video: Satisficing and Optimizing metric
- Video: Train/dev/test distributions
- Video: Size of the dev and test sets
- Video: When to change dev/test sets and metrics
- Video: Why human-level performance?
- Video: Avoidable bias
- Video: Understanding human-level performance
- Video: Surpassing human-level performance
- Video: Improving your model performance
- Reading: Machine Learning flight simulator
- Video: Andrej Karpathy interview
- Video: Carrying out error analysis
- Video: Cleaning up incorrectly labeled data
- Video: Build your first system quickly, then iterate
- Video: Training and testing on different distributions
- Video: Bias and Variance with mismatched data distributions
- Video: Addressing data mismatch
- Video: Transfer learning
- Video: Multi-task learning
- Video: What is end-to-end deep learning?
- Video: Whether to use end-to-end deep learning
- Video: Ruslan Salakhutdinov interview
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
The course is very practical and also leads you to learn the real challenge you will encounter while working on machine learning project. While it's easy to follow as the previous courses, you need to think more strategically. I would recommend bringing an idea or a project you are planning and apply what you learned here.
It was great.
Great course! Excited to learn more!
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Very helpful for DL Projects
#Empirical #Applied