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Отзывы учащихся о курсе Introduction to Machine Learning in Production от партнера

Оценки: 776
Рецензии: 150

О курсе

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...

Лучшие рецензии

4 июня 2021 г.

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

19 мая 2021 г.

Excellent course, as always! Many thanks!\n\nGreat combination of theory + notebooks with practical examples.\n\nEverything is perfectly structured. I will recommend this course to everyone!

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1–25 из 175 отзывов о курсе Introduction to Machine Learning in Production

автор: Francisco R

21 мая 2021 г.

I know it's an introduction, but I got a bit disappointed. It's quite basic and even though it has some hands on notebooks, they're optional and you don't need to work on anything. Quizzes are easy, and I didn't have the feeling I learnt much. I'm still rating it with 3 because, well, it's Andrew Ng, and this his teaching is worth gold.

автор: Snehotosh B

22 мая 2021 г.

I found the production part absent and is another ML course.

автор: Mohamed A H

14 мая 2021 г.

I give you the full review stars since I learned many new things that I did not pay attention to before, e.g.: I used to focus on models for many years instead of data.

автор: HARI A K

16 мая 2021 г.

Really good for anyone with strong background in DL and ML... And want to be able to start a real time project... Or lead a ML team

автор: Wesley E B

16 мая 2021 г.

It had some great advice for how to design a machine learning system. More practical examples would have been appreciated.

автор: Rajesh R

22 июня 2021 г.

Most of the discussion was theoretical. Some useful knowledge but not useful for real world MLOps

автор: Kyung-Hoon K

16 июля 2021 г.

Thanks, Andrew!!!!! Your sharing real-life experiences was invaluable. This was super special as it has opened my eyes beyond the ML-code. I've realized what I have to do in my real job. I will spend more time on communicating with business teams to close the gaps on different metrics expectations. I will shift my mindset from code-centric to data-centric. I will check out my data before my team dives into the ML coding itself. Thanks, Andrew and the team!!

автор: Picioroaga F

13 июля 2021 г.

This course was one of best that I've taken regarding the ML. I think this course should be the starting point for each student who would like to pursue a career in ML and AI. Understanding the problem in the business context before jumping to the solution, understating the data in the same context, are the key ingredients for defining the success of a "product/service" involving AI.

автор: Deepak K

14 мая 2021 г.

it was good to learn

автор: Koke H

3 июня 2021 г.

All pretty trivial

автор: Omar A

20 июня 2021 г.

I liked how Andrew is able to simplify difficult and tricky concept without making you feel uncomfortable about lacking the knowledge. Everything is smooth and up to the point. In addition, the labs are interesting and highly related to the material. Overall, the concepts taught are very helpful and important to make you an real machine learning engineer not just a one who copy and paste bunch of theories, codes, ....etc.

автор: Keith K

5 июля 2021 г.

I found that the course is quite useful and practical. I enjoy a lot watching Andrew's Lectures especially when he used many examples from his previous projects in his career , giving good demonstration of common challenges in ML model development as well as maintenance/monitoring in production. The course is well designed and gives us a very clear foundation about Machine Learning in production.

автор: Muhammad D

22 июля 2021 г.

I find this to be a very philosophical approach to Machine Learning, especially where Andrew NG poise questions that will make you rethink entirely the way you've previously approached ML Problems. The explanations are broken down in a manner that makes it so seamless to grasp. Thank you for this opportunity.

автор: Tamim-Ul-Haq M

13 июня 2021 г.

Incredible course. It describes in detail of how machine learning engineering is done in a production environment. It takes the aspects learnt for Course 3 (Structuring Machine Learning Projects) from the Deep Learning Specialization (also taught by Andrew Ng) and provides an even more in-depth knowledge base

автор: Engin K

11 сент. 2021 г.

T​he course goes through methods to solve common operational problems that data scientists experience all the time but are not either aware of the problem or do not know how to solve the problem. All the methods are explained clearly with some practical examples. One of 'must take' courses by Andrew NG.

автор: Baturalp M

30 мая 2021 г.

Great for beginners but I also ejoyed it since it nicely tidies the practical knowledge that an experienced ML engineer/data scientist gains throughout his work. Overall, it's a good polishing over my knowledge and learned some new points that I didn't paid enough attention to.

автор: Mindset N

16 июня 2021 г.

This course is very hands-on. It clearly teaches Machine learning beyond python notebook. I enjoyed this course and currently taking the second part of this specialization "Machine Learning Data Lifecycle in Production". Great content from Andrew Ng and Robert Crowe.

автор: Gaurav G

23 мая 2021 г.

Awesome Course.... :) Really I enjoyed a lot. I completed this 3 weeks of course just in 4 days along with my office work (too much interesting).Very helpful... Very knowledgable... Thanks Andrew Ng for the course. A big thank to DeepLearning.AI team.

автор: Cristiano G

11 июня 2021 г.

Very nice course! The field of MLOps is not so well documented and fortunately we have very experienced professionals able to share their expertise. The content is very clear and the examples provided by the professor are extremely insightful.

автор: Dennis D

21 мая 2021 г.

Even after having worked several years in the role of an MLE there were some useful ideas here and there that I'm excited about applying in the future. Overall, everything was very clear and understandable. I liked the lab about deployment.

автор: Hernán Q

23 июля 2021 г.

It covers a lot of the real world problems data scientists find when trying to build machine learning solutions. Many of the best practices reviewed here are a common sense thing but having it wrapped toghteter here was really great !

автор: Nilesh G

26 июня 2021 г.

Deep learning courses are always best, cover all aspects in theoretical as well as more emphasize on practical knowledge which helps a learner ready for the real life challenges in Data science domain...Thank You Andrew NG and Team

автор: Paulo A A M

26 июля 2021 г.

Excellent course!! A new way to understand the key factors to master the Machine Learning lifecycle. This is much more than one course, this is an invitation to change our mindset through an exciting journey with Andrew Ng!!

автор: Taku F

6 июня 2021 г.

The course was fairly compact and you would be able to finish each week lesson every day if you eager to do so. It was fun and educational. I loved the surprise in the last question of the optional quiz in week 3.

автор: Motilal R S

13 июня 2021 г.

Great course explaining concepts on ML lifecycle and deployment, especially touching topics like concept and model drift, monitoring models, error analysis, experiment tracking, pipeline and lineage. I loved it.