4.6
4,223 个评分

## 课程概述

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training....

## 热门审阅

OD

May 30, 2020

Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging. Hope to see the instructor soon in a another course. Thanks so much. I learned a lot.

PT

Dec 1, 2018

This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)

## 426 - Launching into Machine Learning 的 450 个评论（共 478 个）

Apr 27, 2019

v

Jun 8, 2018

I've spent the past three years studying ML and AI starting from the ground up with Calculus, Linear Algebra, basic data science techniques and eventually Deep Learning. I am primarily interested in this specialization because I would like to begin using GCP professionally. This course provides a very quick surface level overview of the "history" of ML and the techniques that have been aggregated to make up the current cutting edge of AI in practice. Already having a grasp on many of the concepts, I was able to zip through this course in a few hours and found it basic. If you're looking for something a bit more challenging, I would recommend the DeepLearning.ai specialization also available on Coursera. This course works well as a refresher and a high level overview. If you are completely new to the field, be warned that there is quite a terminology to be unpacked that is covered more thoroughly in other courses on Coursera. The University of Washington machine learning specialization (though sadly cut short) would be a much better starting place, if you are completely new to the topic.

Jul 10, 2019

Contingency tables and ROC graphs were poorly characterized and presenter resorted to obfuscation to mask his unfamiliarity with this basic statistical concept. Furthermore, when the proposed task is to "Identify pictures containing house cats", correctly identifying a picture that does not contain a house cat (True Negative) does NOT count as a successful prediction. You are confusing sensitivity with specificity in your so-called confusion matrix.

With respect to labs, you should warn students to leave their notebooks open so we do not have to reload everything. Also in the cab fare exercise the presenter did not elaborate on the fact that the RMSE's were higher than the predicted fare and mistakenly excluded time of day when in fact fares increase during rush hour.

May 22, 2018

Using hash function doesn't seem a good way to split the dataset:

-You could discard a relevant feature

-You will group data on a similar characteristic, which might not represent the population well

-You don't have control over the size of your split since the feature will not likely be uniformly distributed

Can't we add an index feature/column and do a modulo on the index?

Sep 23, 2018

In The last lab, teacher says that there is 100,000 in data set , then we extract 10,000 from data set.

But there is 1,000,000,000( I checked by

'''SELECT

COUNT(vendor_id)

FROM

nyc-tlc.yellow.trips'''9

SELECT

COUNT(vendor_id)

FROM

`nyc-tlc.yellow.trips)

In that context, I think MOD(...) meaning is totally different ?

Jul 27, 2019

I feel that the flight and taxi cost estimation was kinda rushed. It was hard for me to follow. Ii having less knowledge about SQL was finding it to be tough. Before that, everything was clean and awesome. I think I have to revisit these courses after learning SQL better.

Aug 10, 2019

good course. but it is just like an intro regarding how to use google cloud platform. but theory part was decent. can give it a try. but lectures were really indulging

Nov 14, 2018

Some good material here, but at times it feels like an ad for GCP. And the labs are not very inventive. You just run a python notebook with canned stuff in them.

Oct 7, 2018

While the concepts covered were good and very valuable, I didn't like the lab sessions. Just having to walk through code is not a good way to get hands-on.

Apr 18, 2020

Too much content for just one week. Exercises solved and not made for students to resolve them. Suggesting more complicated tasks is not teaching.

Nov 29, 2019

I could only sustain it because I have completed basic ML courses earlier. Too many tech concepts & jargons overloaded in a very short time.

Jun 14, 2019

Learning the approach was very valuable. The exercises were just copy and paste of a bunch of code that it isn't expect we understand.

Aug 29, 2018

It felt too hard. I liked because it gives a very good idea but the concept was too hard especially with the math involved

Jan 15, 2020

This course is just ok. It is not interactive and I don't feel that I learned much when compared with other ML courses.

Sep 28, 2019

The course is good, but I missed the hands on part. You really do not need to code. That should be changed.

Jan 5, 2019

not enough practical content such as types of machine learning and different algorithms to be used etc

Jul 27, 2020

In the labs, I kept getting disconnected from the Jupyter notebooks, and had to keep reloading them.

Jun 11, 2018

Course includes good presentation material which unfortunately is not available to download.

Aug 23, 2018

If you already know ML there isn't much in this course that will be value addition for you.

Jul 29, 2018

The course is very well explained, but I was already aware of most subjects.

Jul 15, 2018

There is a little more content here than in the 1st course.

Aug 11, 2018

Apr 29, 2020

Talked too much...more practical example step by step

Sep 19, 2019

A bit difficult when introducing the ML history

May 23, 2020

quiet difficult to undertand