Chevron Left
返回到 Machine Learning in the Enterprise

學生對 Google 云端平台 提供的 Machine Learning in the Enterprise 的評價和反饋

1,403 個評分


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....




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.



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.


101 - Machine Learning in the Enterprise 的 119 個評論(共 119 個)

創建者 Attila B


Really good course with a lot of practical examples.

創建者 Pratik S


complete hyper parameters is given in lab

創建者 Ruslan A


Many notebooks contain some typo/erros.

創建者 Wang Y


best course in the specialization!!!

創建者 Gaurav B


I was looking for more hands-on.

創建者 Sarwar A


Good course overall

創建者 Swaraj P


Nice tutorial

創建者 KyeongUk J



創建者 Matthew B


Labs were very confusing. Explained theories well but in practice didn't really learn much. I wouldn't recommend if you're a beginner. Google has a very interesting way on teaching.... On that note they should stick to building tech, never teaching. Didn't really learn how to build anything in ML, sort of skimmed on some API's they offer. In reality, the first course was probably the best... The rest of the specialization was just a rinse and repeat sort of thing.

創建者 Bhargav D


Great course must should make labs compulsory and not provide solution it takes away the fun of thinking.

創建者 Siddharth A


I felt that hand-on or explanation was not sufficient. Coverage is good.

創建者 Alberto C


There are some lessons where the concepts are exposed in a too fast way

創建者 Rahul K


Some tough concepts !!!

創建者 Pablo I F


Very bad english subtitles. For non-english speakers, the subtitles doesn't help, but it confuse what the teacher is explaining. It takes me a lot of time to understand some parts of the course

創建者 Mike W


The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

創建者 Arman A


Pros: Tensorflow is an excellent framework for deep learning

Cons :

1- The way this material is designed is 10 X SHIT

2- Either teach properly or don't teach at all.

創建者 Mohannad B


A lot of inaccurate data, please check deep learning ai specialization for more accurate info. this is good for introducing you to GCP not the concepts of AI

創建者 Radha M K V


Very redundant and superficial.

創建者 man c y


poor labs