この 1 週間の集中動画セミナーでは、Google Cloud Platform（GCP）のビッグデータと機械学習に関する機能をご紹介します。受講者は Google Cloud Platform の概要と、データ処理機能の詳細について学ぶことができます。 このコースを完了すると、以下のことができるようになります。 • ビッグデータと機械学習に関連する Google Cloud Platform の主なプロダクトの用途と価値の特定 • CloudSQL と Cloud Dataproc を使用した、既存の MySQL と Hadoop / Pig / Spark / Hive ワークロードの Google Cloud Platform への移行 • BigQuery と Cloud Datalab によるインタラクティブなデータ分析の実行 • Cloud SQL、BigTable、データストアからの選択 • TensorFlow を使用したニューラル ネットワークのトレーニングと使用 • Google Cloud Platform のさまざまなデータ処理プロダクトからの取捨選択 このコースに申し込むには、以下の 1 つ以上の分野で約 1 年の経験が必要となります。 • SQL などの一般的なクエリ言語 • 抽出、変換、読み込みの各アクティビティ • データ モデリング • 機械学習と統計 • Python でのプログラミング Google アカウントについての注意: • Google Cloud Platform の無料トライアルに登録するには、Google / Gmail アカウントとクレジット カードまたは口座番号が必要です（Google のサービスは、現在中国ではご利用いただけません）。 • 請求書の送付先住所が欧州連合（EU）またはロシアの Google Cloud Platform ユーザーは、https://cloud.google.com/billing/docs/resources/vat-overview の VAT 概要文書をお読みください。 • Google Cloud Platform の無料トライアルに関するよくある質問については、https://cloud.google.com/free/?hl=ja をご覧ください。 Looking for the English version of this course? Check out https://www.coursera.org/learn/gcp-big-data-ml-fundamentals/ Buscando la versión en español de este curso? Visita https://www.coursera.org/learn/gcp-big-data-ml-fundamentals-es/
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來自 Google Cloud 的課程
Google Cloud Platform Big Data and Machine Learning Fundamentals 日本語版
15 個評分
與講師見面
Google Cloud Training
As an introduction, my name is Valliappa Lakshmanan, everyone calls me Lak.
I'm at Google.
I am part of the Professional Services organization at Google where I
advise customers who are moving to Google Cloud on the best ways to do them.
I focus primarily on the big data and machine learning parts of Google Cloud.
Before I joined Google, I was a research scientist at NOAA,
which is the US weather research organization.
It was a cooperative institute between NOAA and the university.
So, I built machine learning algorithms for weather prediction for
flash floods, hail, tornadoes, etc.
And that's basically what my background is.
I have a PhD in Electrical Engineering, and
I was working on machine learning for weather.
Until about maybe like, three years ago,
I took a leave of absence from my university academic job,
went to a start-up that was doing precision agriculture.
And there, I headed up a data science team that built a rainfall prediction system.
And it was in the process of that that I discovered the cloud.
Now everything that we had done at the university, this was,
remember this was like more than three years ago, it was primarily on premise.
We had a cluster of machines, and
it was on that cluster of machines that we ran our real time systems.
That we did our experiments, that we did our, and machine learning training,
everything happened in that cluster of machines.
And then we went to the startup where everything happened on the cloud.
We didn't have the cluster of machines, we didn't have any machines.
All we had were laptops, and we ran around with our laptops.
So anything that we need to do any massive computation that
we needed to do would happen on the cloud.
And that was when I realized, one of the key economic advantages of running
things on the cloud, because whenever we needed to do a large scale study,
we would have our data on a tape drive,
we'd move it over to our cluster of machines, do it, move it off, etc.
And this was a whole bunch of data handling.
And what kind of discovered was the kinds of things that would take us four years
to do on-premise in our own cluster, we were doing every two weeks at the startup.
And it was not that they were any smarter at the start of the [INAUDIBLE] than we
were at Noah, or the government.
It was essentially that rather than do things on a limited environment,
we would basically scale thing out to a lot more machines,
and get things done a lot faster.
And if you're looking at innovation that happens,
things that take four years now take two weeks.
The number of things that you can try, and
the number of ways that you can do it, just explode.
And the speed of innovation just becomes a whole lot better.
So, when I was looking at, okay, what do I do next?
Now that I discovered the cloud, and I discovered what being able to do
very fast innovative cycles with machine learning on the cloud could achieve.
I wanted to be at a place where I could do both ML and cloud and that was Google.
And so my goal for this course is to get you all as excited about cloud,
about big data and about machine learning as I am.
