Chevron Left
Retour à Structuring Machine Learning Projects

Structuring Machine Learning Projects,

26,194 notes
2,826 avis

À propos de ce cours

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....

Meilleurs avis

par 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.

par WG

Mar 19, 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.

Filtrer par :

2,787 avis

par Sebastian Kraus

Apr 23, 2019

At the beginning I thought this course might be a bit obsolete. However, I'm still taking a look at the material provided even aufter i finished the specialization.

par Lee Chin Poo

Apr 23, 2019

Sincere thanks to Assoc. Prof. Andrew Ng and team for sharing the fantastic ideas about deep learning!

par Maciej Filipowicz

Apr 22, 2019

Great course, the conceptual thinking is as important as knowing what the algorithm does. Thanks Andrew!

par Shimin Zhang

Apr 22, 2019

nice courses!

par Saurabh Sharma

Apr 21, 2019

Great courer that will leap forward my actual DL project experience

par Gyuho Song

Apr 21, 2019

It is such a masterpiece in Neural Network. I feel like I am fully equipped now and I cannot wait to dive into CNN and RNN!

par Jiapeng Zheng

Apr 20, 2019

It really helps me a lot. I like the style of Professor Ng very much!

par Bharath N Shastry

Apr 20, 2019

A lot of concepts were put forward and taught well. If there was a programming assignment as well to back up the concepts that were taught like multi-task learning, how to deal with data mismatch, dividing the total data into train\train-dev\dev\test data etc.

par Dustin

Apr 20, 2019

Nice structure and organization. Some of the assignments are really tricky, and its good for me to revise some of the course materials.

par Ramakumar J A

Apr 20, 2019

Excellent course. So much of learning and expert insights for folks curious about ML and NN.