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Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

4.8
stars
49,877 ratings

About the Course

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....

Top reviews

ED

Aug 22, 2020

Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.

MG

Mar 30, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

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1076 - 1100 of 5,718 Reviews for Structuring Machine Learning Projects

By Виктор В К

May 15, 2020

Очень интересный курс. Много узнал о стратегиях развития проектов с использованием нейронной сети. Спасибо отличному преподавателю Andrew Ng.

By Pree R

Feb 15, 2020

great content and fantastic way to practically learn various aspects of a real-time ML project. Appreciate the great instructor & lectures!!!

By Max A

Jan 2, 2020

Despite working on ML projects for two years now, found a lot of insights in this course.

Thank you all for making these incredible materials.

By Krishna B

Mar 12, 2018

Very good class. There was a lot of practical knowledge in terms of fitting models and setting up a workflow that are hard to find elsewhere.

By Shabie I

Feb 10, 2018

Some very sensible advice given in the lecture about how to properly evaluate the ML models though at times the lectures felt a bit too long.

By Ahmed M K

Apr 15, 2021

The case studies were really challenging and helped me a lot to understand and realize how real decisions are being made with such projects.

By Gaurav V

Jun 11, 2020

Very Productive Skills were taught which helped me very much to reduce my time and effort on the model and come up with maximum productivity

By Di C

Mar 27, 2020

Great course for strategic part in ML projects. The project-based simulator is a good way for exercising the ideas learned in the lectures!

By Yashveer S

Mar 15, 2020

I enjoyed this course the most thus far because it related practical experience in the real world, which I currently do as a data scientist.

By yogurt c

Jun 12, 2018

I know that's really hard to put code section here. But that's necessary for giving example to explain how to put all the knowledge together

By Keval N D

Jan 16, 2018

An excellent course. This course has some valuable insights on how to organize and systematically move forward in machine learning projects.

By Julian F V

Oct 27, 2017

Excellent approach in how to solve difficult questions about, wich path to take, related to improve the deep learning models, just excelent!

By Duncan M

Sep 7, 2017

Really valuable insights into how to make progress and *think* about machine learning projects, and taught in an engaging and practical way.

By Laurent J

Aug 22, 2017

This course is quite unique in its content and is of great help to guide you in the plethora of options that Deep Learning algorithms offer.

By Kamran K

Apr 11, 2021

I would prefer to be the student of Sir Andrew Ng.

I salute Sir Andrew for encouraging me to follow him till to complete the specialization.

By richa k

Oct 23, 2020

This course is worth taking. it covers advance level though basic topics that helps you deal with machine learning projects in smarter way.

By Deepak P

Jul 10, 2020

Unique material as claimed, provides an opportunity to practice different decision making skills that are common in a ML practioneers life.

By Yu L

Apr 20, 2020

valuable insight on how to build a machine learning model, most of the tricks are omitted in a college course but it is useful in practice.

By Victor Y

Feb 14, 2020

Excellent course on learning how to plan for different stage of a Deep Learning project and common potential issues people would encounter.

By Peter K

Dec 4, 2018

Excellent course and I love the assignments makes you think. Learned more from getting them wrong than the ones I got right the first time.

By Souraj M

Nov 19, 2017

Very good course. Will take it multiple times I guess.

Nowhere I could find this material and it would really help me in my day to day task.

By Liudmila K

Jan 24, 2022

Excellent course! A lot of practical information on how to make decisions while training your model based on years of Andrew's experience!

By ossama a

Dec 4, 2021

I loved this course as it considers things from a big picture view , on what the problems are and how to solve each of them systematically

By K K J

Sep 19, 2020

This course is really good for team leaders as you can understand what to do in some real world problems. The simulation part was the best

By sanjeevi g

May 15, 2020

It was fantabulous teaching from Andrew Ng, in a short period of time I could build up the knowledge on deep learning in an efficient way.