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Avis et commentaires pour d'étudiants pour Introduction to Machine Learning in Production par deeplearning.ai

4.8
étoiles
1,647 évaluations
287 avis

À propos du cours

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline...

Meilleurs avis

RG

4 juin 2021

really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value

IU

5 déc. 2021

I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!

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251 - 275 sur 326 Avis pour Introduction to Machine Learning in Production

par Serhan Ç

2 août 2021

Sensational!

par Daniil Y

6 sept. 2021

E​xcellent!

par Sanjay H

19 juil. 2021

Good Course

par Luis C M R

7 janv. 2022

Excellent!

par Anugraha S

17 mars 2022

Wonderful

par EDUARDO S

7 nov. 2021

Excellent

par VENKATA S R K

7 sept. 2021

very good

par Thành H Đ T

29 juil. 2021

thank you

par Roberto C

19 juin 2021

Excellent

par Ramil J

22 mai 2021

Fantastic

par Marcelo B

10 janv. 2022

AMAZING!

par Vyacheslav K

25 mai 2021

Perfect!

par Khizar S

7 juin 2021

love it

par Amin T

5 juil. 2021

Great!

par Trung N H

21 sept. 2021

g​ood

par Aman K d

21 avr. 2022

good

par Preetam G G

20 avr. 2022

nice

par Duc A L

11 oct. 2021

Good

par Willah M A

8 août 2021

nice

par MohammadSadegh Z

17 juil. 2021

par Ajit k

27 mai 2021

T​his course help learner to gain key insights from one of the leader in AI field, for developing Machine leanring based applications. Course is keep more on discussion and thoughts than technical (more provided through ungraded lab exercies).

S​uggestion:

I​t would have been better if the graded labs was made part of the grading and had more lab exercies on fastAPI and other topics. (I think, the purpose of the course is to teach it to a larger audience including non-tech people).

I​ enjoyed and learned a lot from the course.

T​hanks.

par Jeffrey B

28 déc. 2021

I was a little disappointed that this was heavily focused on unstructured data, but it was still a wonderful course. Many of the techniques of being "Data Centric" do not carry over as well to structured data. I am hoping I will hear more in the next courses of this specialization that address being data centric with structured data (which would seem to be more applicable to many business analytics cases).

par Cristian C H

21 oct. 2021

While the overall content of the course for ML LifeCycle is great, the examples and general assumptions are for supervised learning and labeled data, in some real scenarios, having labeled data is just not possible but by no means this indicates there is no possible AI solutions and models that give business value. So a little inclussion of unsupervised and semisupervised learning examples would help.

par Yoshihiro H

13 oct. 2021

This course is a practical guide for someone who's interested in developing ML models in real life, make use of it and maintain, improve, and support it for business needs. To those folks whos coming from an academic background and haven't seen the landscape of the use of ML models in real life, this course can be a really good starting point ;).