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: This course is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and set prioritization for their work or their team's work.
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 |
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
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Very nice course that explains well how to structure and plan your machine learning projects. I especially liked the case studies!
This course is a summary of Andrew's experience. I've yet listened this nuts and bolts from Andrew speech(you can find it on youtube) but there are some precious advice that are so much valuable. I'll recommend this course to everyone who want to start a carer in DL. Big thanks to Andrew, the Deeplearning.ai team and Coursera.
Amazing course contents and case studies to get a solid understanding of key Deep learning concepts and applications in real world scenarios...very helpful for freshers in Deep Learning like me.
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Lots of practical stuff about training models. But you should try building a few models before doing the course. Otherwise, you may not fully appreciate how much time can be wasted unless you use Andrew's clear and logical approaches.