Вернуться к Structuring Machine Learning Projects

звезд

Оценки: 47,200

•

Рецензии: 5,421

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....

AM

22 нояб. 2017 г.

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.

JB

1 июля 2020 г.

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

Фильтр по:

автор: Mangesh

•18 мар. 2018 г.

I took this course soon after completing the Machine Learning course, before starting the Neural Network and Deep Learning. And found it extremely helpful, the simulator approach takenup in the course is absolutely spot-on and unique to this course (as compare to any knowledge source on internet).

Andrew NG has poured in his tacit knowledge and made it explicit in the best possible way !

автор: RUDRA P D

•10 июня 2020 г.

This course gives insight to all the errors and their analysis, different approaches to deal with problems in machine learning and also working of different models such as Face recognition, Speech recognition and Automated driving models. Andrew sir explains all this concepts in a very learnable manner. I do recommend this course to those who are going to build their first ML model.

автор: Ber L C

•31 мар. 2018 г.

One of best courses I have taken on Coursera. There are not much available online resources to learn about how to structure and manage a Machine Learning projects. I would like to express my appreciation for all of the hard work and dedications professor Andrew Ng and his team spent on designing such a great course with understandable lectures as well as well-designed assignments.

автор: Armando G

•30 сент. 2018 г.

This course is the most hands-on deep learning class I have seen so far... and have taken a lot. Most courses focus on the technical details of feedforward, backpropagation, activation functions, etc. but this is the only one I have seen where guidance is provided on how to tackle real-life situations. So far, the BEST course I have takes on deep learning projects tips and tricks.

автор: Dennis O

•16 дек. 2017 г.

This course is light on math and programming but loaded with great advice that I have already been able to put into practice at work. Some things are lessons I have learned by being in the field for a few years and others are lessons that might have taken a while to learn on my own. This course has extremely valuable real-world advice that will impact the work I do right away.

автор: Artyom K

•19 мая 2019 г.

I understood such concepts as: evaluation metric, percentage of distributions, estimating train and dev set errors,

training a basic model first,

choice

softmax activation,

carrying out error analysis

on images that the algorithm got wrong,

algorithm will be able to use mislabeled example,

dev and test set should have the closest possible distribution to “real”-data, and so on.

автор: Sherif M

•11 апр. 2019 г.

This course offers insights into organizing and structuring machine learning projects. It is different than the other courses of this specialization by not going to much into technical details. I found it still very rewarding since Andrew offers some very niche tricks that can help researchers in practical application of machine learning and deep learning algorithms.

Great job!

автор: Oscarzhao

•5 мар. 2018 г.

The topics discussed in this class are very closely associated with the title `Struturing Machine Learning Projects`. These topics are more than just concepts, I think they would be very useful in real projects (Though I haven't done one :) ). There are a lot of use cases discussed in the course. Hoping in the near future, I have an opportunity to use them in practice.

автор: Michalis P

•18 окт. 2019 г.

This course was smaller and a bit more theoretical than the previous two courses. Although the lectures give you a good insight on error analysis, things to check in order to optimize your model and finally how you can use a pre-trained model to solve a different task - of the same input data type.

Thanks both to the instructor and the crew for this great series of lectures.

автор: Bill A

•15 мая 2018 г.

Really changed my thinking about how to run an ML project. I just wish my projects were the kind that could exploit these methods to the fullest. They're more like the autonomous driving example. There are parts that DL is useful for (particularly sequence learning with RNNs) but big parts that aren't (e.g. use of probabilistic graphical models). Anyway, awesome course!

автор: Linghao L

•3 янв. 2018 г.

Lots of principles and skills about how to organize machine learning projects and diagnose problems. Especially for the error analysis part, you will definitely save much more time in solving these errors than you expected by following the suggestions taught by Andrew. Thanks Andrew, I really learned a lot from your awesome deep learning courses and felt closer to industry.

автор: Chetan P B

•18 апр. 2020 г.

This course is just magical. It covers so many concepts that would require years of experience to gain. Thanks to Professor Andrew for sharing his great knowledge with us. The bias/variance and train and dev/test distribution concepts are very well explained with examples. Also, the quiz helps to practice these concepts which require a better understanding of all of these.

автор: Pedro H d O P

•23 февр. 2018 г.

Great course as always! Andrew Ng is a great teacher, and he actually can inspire all of us on being better professionals (and researchers) on the field. The idea of the case studies was great! It was very fun to experience how it is to be part of deep learning projects and the decisions associated with this. Congratulations for all of you guys from coursera! Thank you!

автор: Adrian S

•26 апр. 2021 г.

This short course focuses primarily on non-technical aspects of deep learning projects. The value of this subject matter is the focus on aspects that can make or break the success of a machine learning project. Given the fact that as much as 80% of deep learning efforts never make it "into production" (Gartner et al) spending time on these issues is highly recommended.

автор: Sahaj J

•2 авг. 2020 г.

Initially, I was bored from some initial lectures. But later, I found that this is one of the most important course in the specialization because it dives to you the handful of experience in a single course which one gets after many years of practicing machine learning. At the end of this course, I am very much enlightened with the content and journey of this course.

автор: Amanda W

•12 сент. 2018 г.

Loved this course as well. Presented very difficult material in a simple and easy to figure manner. Excited for more! Thank you to those who dedicate their time to making this course available, and taking the time to answer questions regarding the material. It is much appreciated and I highly recommend these courses to those who wish to learn about Deep Learning.

автор: Mohammed M

•29 нояб. 2020 г.

Really great course. It is very helpful to gain knowledge on the basic strategies to consider while approaching a Machine Learning problem. The assignment quizzes present you with a real-world ML problem (case study) and asks you questions on what you would do when presented with different situations. So that's a great way to get some insight on how things happen.

автор: Swakkhar S

•4 авг. 2020 г.

This is a great course, unlike many other courses where you put 1/2 lines in between the code completions and pass the assignments. This one has got a number of issues where one has to be able to think about the problem and the data/model/metrics on hand to analyze and take further steps. Once again this one is from one of the top instructors of the world. thanks!

автор: Nihar P

•31 янв. 2021 г.

This course has given me insights into the importance of choosing a better ML pipeline. Not only knowledge of ML is important. We must know when and where and how to apply it our your problem. This course taught me more about that. Thanks to Coursera, if I would have taken this class in school I must have missed this gemstone information.

Thank you, professor NG.

автор: Ventsislav Y

•22 дек. 2018 г.

Awesome course! I really like the explanations by Andrew Ng. This course gives you skills about how to make error analysis on your models, how to build a machine learning strategy, importance of single evaluation metric, satisficing and optimizing metrics, setting up the train/dev/test distributions and many other topics. Highly recommend this course to everyone!

автор: Himanshu B

•6 июля 2018 г.

This course is surely gona help if planning to learn deep learning.Gaining knowledge is not the best part unless you don't know how to apply the knowledge. This course is all about how and where to apply machine learning and deep learning concepts with much more practicing in real life case studies. Thanks alot for providing such a great content and case studies.

автор: Mukund C

•14 окт. 2019 г.

Excellent course. I loved the "flight simulator". I found them challenging. However, some of the questions were worded confusingly, so I got the answers wrong. There is no point in trying to "trick" the test taker by confusing wording in the question as well as in the answers. But, I think this course provides a pragmatic approach to machine learning projects.

автор: Barbara T

•25 дек. 2018 г.

This class was well worth the time if you've already invested some effort in learning different principles of machine learning. It causes you to reflect back on different implementations, and understand better how to set up a potential problem and determine how to improve it. The many examples helped solidify items in lectures from prior courses in my mind.

автор: Jagdeep S

•29 окт. 2017 г.

This course imparts the real world experience that Andrew gained by working in the Industry on the bleeding edge of AI and Machine Learning. This class saves at least 2 years of painful learning on your own by trial and error. I think 2 weeks on this course will put you ahead by 2 years in your path of building neural networks for solving real world problems.

автор: Sreevishnu D

•19 окт. 2020 г.

This specialization only gets better and better. All the courses are amazing and this course is no different. Best content and teaching as always. Thanks for having thought of ways to provide conceptual, practical and intuitive understanding of the topics and delivering it in the form of these wonderful courses.

Thanks Andrew Ng, Deeplearning.ai and Coursera.

- Аналитик данных Google
- Управление проектами от Google
- UX-дизайн от Google
- ИТ-поддержка Google
- Наука о данных IBM
- Аналитик данных от IBM
- Анализ данных с помощью Excel и R от IBM
- Аналитик по кибербезопасности от IBM
- Маркетинг в социальных сетях от Facebook
- Разработчик комплексных облачных приложений IBM
- Представитель по развитию продаж от Salesforce
- Сбытовые операции Salesforce
- Soporte de Tecnologías de la Información de Google
- Certificado profesional de Suporte em TI do Google
- ИТ-автоматизация с помощью Python от Google
- Tensorflow от DeepLearning.AI
- Популярные сертификаты по кибербезопасности
- Популярные сертификаты по SQL
- Популярные сертификаты по ИТ
- Посмотреть все сертификаты

- бесплатные курсы
- Изучите иностранный язык
- Python
- Java
- веб-дизайн
- SQL
- Cursos Gratis
- Microsoft Excel
- Управление проектами
- Безопасность в киберпространстве
- Людские ресурсы
- Бесплатные курсы в области науки о данных
- говорить на английском
- Написание контента
- Веб-разработка: полный спектр технологий
- Искусственный интеллект
- Программирование на языке C
- Навыки общения
- Блокчейн
- Просмотреть все курсы

- Навыки для команд по науке о данных
- Принятие решений на основе данных
- Навыки в области программной инженерии
- Навыки межличностного общения для проектных групп
- Управленческие навыки
- Навыки в области маркетинга
- Навыки для отделов продаж
- Навыки менеджера по продукту
- Навыки в области финансов
- Проекты по разработке для Android
- Проекты по TensorFlow и Keras
- Python для всех
- Глубокое обучение
- Навыки Excel для бизнеса
- Основы бизнеса
- Машинное обучение
- Основы AWS
- Основы инженерии данных
- Навыки для аналитика данных
- Навыки для UX-дизайнеров

- Сертификаты MasterTrack®
- Профессиональные сертификаты
- Сертификаты университетов
- MBA и другие дипломы в области бизнеса
- Степени в области науки о данных
- Степени в области компьютерных наук
- Дипломные программы по анализу данных
- Степени в области общественного здравоохранения
- Степени в области социальных наук
- Дипломные программы в области управления
- Дипломы ведущих европейских университетов
- Дипломы магистра
- Степени бакалавра
- Дипломы с карьерными путями, ориентированными на результат
- Бакалаврские курсы
- Что такое диплом бакалавра?
- Сколько времени нужно для получения диплома магистра?
- Стоит ли получать диплом MBA онлайн?
- 7 способов оплатить магистратуру
- Просмотреть все степени