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Structuring Machine Learning Projects, deeplearning.ai

4.8
23,802 calificaciones
2,636 revisiones

Acerca de este Curso

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....

Principales revisiones

por AM

Nov 23, 2017

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

por TR

Sep 22, 2018

This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.

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2,602 revisiones

por Xuanhao Zhang

Feb 16, 2019

Not that useful for me

por QIUSHI HUANG

Feb 16, 2019

not quite useful

por Andreea Alexandru

Feb 16, 2019

Liking this course is subjective. It is indeed based on the experience of others, but since experience can't always be generalized and transferred, the lectures are repetitive and bland (they are also badly edited in Week 2). On the other hand, the two "ML flight simulators" are really interesting and answering them is not obvious. It requires a lot of thinking and focus to choose correctly from apparently equivalent solutions, which might happen in real projects.

por Matei Ionita

Feb 16, 2019

I'm glad I spent some time on the "Flight simulator" assignments in this course. It's the first time in the specialization when I actually found the quiz questions challenging, and that's a welcome change. However, I didn't learn too much from the lectures. They were too repetitive, either repeating themselves or the material from the previous course. One or two videos could also do with better editing work: I could hear Andrew making a soundcheck, and there's a 30sec segment that's played twice in a row. Overall, it's probably worth doing this course, given that it requires very little time, and the assignments are useful.

por Matt

Feb 15, 2019

The flight simulators' results were not consistent with the advice provided in the lectures. I'd suggest being either less black and white in the simulators' answer responses, or, being more polarised (more black and white) in the advice provided in the lectures. Otherwise, this is a 5 star course. Many thanks!

por DEEPAK REDDY CHIRTHANI

Feb 15, 2019

Learning the practical tips for implementing the DL algorithms and several ways for improving their efficiency through the best professor. Awesome!

por Carina Li

Feb 15, 2019

I think this especially useful when getting into a real deep learning team.

por Guillermo Florez

Feb 15, 2019

Excellent course, thank you!

por TanBui

Feb 15, 2019

Very good tips on how to fix/improve the training model.

por Subhadeep Dash

Feb 14, 2019

Great course on how to tackle various issues while handling real data. I thank Coursera and Deeplearning.ai for providing such a course that everyone could make use of.