Chevron Left
Retour à Machine Learning in the Enterprise

Avis et commentaires pour d'étudiants pour Machine Learning in the Enterprise par Google Cloud

1,401 évaluations
121 avis

À propos du cours

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model. You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker. The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models....

Meilleurs avis


30 déc. 2018

thanks for the great work. There is so much to learn and I appreciate the effort you made to break things down and providing lab while making the hard decisions on what to commit.


6 juin 2020

This course is so really good to learn about the general knowledge and skill of Data Science like optimization batch or regularization and so on with Google Cloud Platform.

Filtrer par :

76 - 100 sur 119 Avis pour Machine Learning in the Enterprise

par Naman M

19 août 2019


par Kamlesh C

13 juin 2020


par Somaiya J G

14 nov. 2018


par Gustavo M

17 août 2018


par Phạm V T

17 avr. 2020


par Manish K

28 août 2019


par MOHD N B A L

24 nov. 2020


par Dr. P S J

25 juil. 2020


par Balasubramanian T K

13 avr. 2020



3 déc. 2019


par Mirza s N

18 sept. 2019


par Fathima j

11 mai 2019


par Bielushkin M

23 nov. 2018


par Atichat P

4 juin 2018


par Fuat A

20 mars 2020

Google provided with me an opportunity to take the specialization for free. Many thanks.

Just a comment: Labs were great. But, it takes long when i needed to start a lab, i.e. Opening a Google account every time and starting a vm. So, it would be great if i could use the same vm for more than one lab assignment.

par Carlos V

1 juil. 2018

Excellent Course, in the Art and Science of Machine Learning, I quite enjoyed the Hyperparameter tuning in the Cloud and all the advanced tips to improve the models performance, thanks Coursera and Google

par Robert L

7 avr. 2020

Sufficient theory to understand the basis of the ML approach with practical insights to help get started with building models

par Vishal K

15 juil. 2018

Nice course however I think it suits folks who have good exposure of ML to take complete advantage of the techniques

par Yuan L

17 avr. 2021

Great content. The course would be better if all the labs are up to date and include all necessary setup scripts.

par Phillip

16 août 2020

The course is difficult. You may need to review some sections because off the amount of information.

par Manish G

30 juil. 2019

The course is quite good and have balance of theory and labs. It is useful course for beginners.

par Phac L T

1 août 2018

It would be nice to have more complex datasets where predictions would be more meaningful.

par Oleg O

20 oct. 2018

Very good course, but probably requires some more hand-on practice

par Joel M

12 déc. 2018

good lessons and in depth coverage of a range of issues

par Hugo H

3 avr. 2020

Good course, pragmatic and full of practical exercises