Have you seen the Make Google do it commercials? Learn how to make Google tackle the tough questions you want to answer for your job or business with 15+ Machine Learning and Big Data labs that can help you answer questions like:
How do self-driving cars know the speed limit? What SKUs will see the most traffic on my retail website when it’s raining? How can I analyze sentiment of my customers’ conversations with my team? And how can I prepare for Google’s Professional Data Engineer certification exam?
- Start with the Baseline: Big Data, ML, AI Quest if you’re new to Google Cloud. Get a basic understanding of GCP tools like Cloud SQL, BigQuery, APIs, Bigtable, Dataproc, Dataprep, and Cloud ML Engine, and explore public datasets like worldwide genomic data. With about five hours of lab work, you can earn your badge!
- Next, tackle more advanced concepts with the Machine Learning APIs Quest. This is where you’ll learn how to teach a self-driving car to recognize speed limit signs, analyze blocks of text for customer satisfaction, and five more use cases. More fun with APIs, plus experience with Awwvision and Dialogflow.
- You’re ready for the next step, the Data Engineering exam practice Quest. These nine labs, designed by Google’s training team, help you practice critical job skills for the role of a Data Engineer. You’ll practice use cases with TensorFlow, an IoT pipeline, BigQuery and Datalab together, and you’ll learn how to correspond your sales data with the weather!
- Thinking about the Data Engineer certification? Try the practice exam to assess your readiness, and don’t “make Google do it” this time (: Set a timer and see how far you can get. How did it go?
- Extra credit: Challenge yourself with some of the most advanced labs on Qwiklabs today. The Google Cloud Solutions II Quest works through complex scenarios to knit multiple services together to solve a problem. The labs in this Quest come directly from engineers who developed these solutions in real-time. Not for the faint of heart! Dig deep into TensorFlow use cases, use BigQuery to explore NCAA data and build custom interactive dashboards with Boke, and more.
Why not try for a ML or Big Data Quest badge? A badge shows your “flight time” with Google Cloud, and differentiates you as someone with hands-on GCP experience. Would one (or more) of these badges look good on your resume?
You want to get the most out of your data. But when you hear words like “public datasets”, “training data models”, and “correlation percentage”, your eyes glaze over. So how do you start taking advantage of Google Cloud’s data tools?
Here are some easy ways to get started.
Watch and learn. Join Priyanka and Heather to learn how to get meaningful insights with Google BigQuery (4:39).
Then, get hands-on practice with a hands-on lab for yourself. Just click the link below the YouTube video and follow instructions. Did you get your free Qwiklabs credits, to take your first BigQuery lab on us? Just be sure to click before April 2. And if you want to double-check your work, go back to the video and follow along with the demo, starting at 3:45.
Ready for a little more of a challenge? Try the Weather Data and BigQuery lab, and practice correlating diverse data sets. And today only, this lab is featured for #1creditwednesday, which means this lab is available for 1 credit (normally priced at 5 credits).
In this lab, you will take a look at analyzing public data with BigQuery – starting with weather data from NOAA and citizen complaints data from New York City.
Using public datasets, you will find what types of municipal complaints are correlated with weather. For example, you will find (not surprisingly) that complaints about residential furnaces are most common when it is cold outside:
What types of big data do you think you might correlate?
Weather and traffic volume on your retail website
Daily temperature in your town and foot traffic on your business’s street
NCAA winning percentages and uniform colors (there’s a lab for that!)
You can find the Weather Data and BigQuery lab here. Special today, the lab is available for 1 credit only (usually priced at 5 credits), so now’s the perfect time to check it out. Click the green button to start the lab. You’ll see a lower price than the screenshot below, today only.
See how the left panel changes? Those are your credentials to log into the Google console. Use these credentials (not your personal credentials) so Qwiklabs covers the cost of all your lab activities.
Next, you’ll need to accept the terms of service for the student account. Don’t worry about account recovery or emails – you won’t ever need to use this account again.
Then you’re off on your lab! Follow the lab manual and at the end of the lab, you will have correlated two massive public datasets using BigQuery. What will you correlate next? We’d love to hear how you applied what you learned in the lab to your real-world data questions, let us know @Qwiklabs!
Suddenly it’s February 2018 – how is your resume looking these days? Learning something new like cloud computing can be intimidating. Especially when you’re learning vital career skills that can impact your future.
That’s why we released 2 brand new Quests, designed to get you up and running in the cloud. That’s 18 new labs, the perfect way to introduce yourself to the cloud. At the end of each lab in these Quests, you have gained a basic understanding of the tool being introduced and how to use it.
Here are the new Quests:
Baseline: Infrastructure Quest: In these ten short hands-on labs, you will get your first-touch experiences with the Google Cloud services. Get training on basic infrastructure building blocks that make up core components of any production cloud environment. Labs like Kubernetes Engine: Qwikstart are meant to give you a “baseline” understanding of what each set of tools can do for you. Perfect if you’re just getting started with Google Cloud, or with the cloud in general! Enroll now.
Baseline: Data, ML & AI Quest: Another “baseline”, you will get your first-touch experiences with a subset of the Google Cloud services that provide tools for working with big (and small!) data and machine learning / artificial intelligence services in Google Cloud, like Dataproc and Bigtable. For some labs (including these two!), you can choose to use either the console or command line – your choice, and either option fulfills the Quest’s requirement. Enroll now.
How do you learn best? Lectures, videos, hands-on experience, maybe all of the above?
Today, we’re asking a different question. Do you prefer to learn in English? Español? 日本語? Qwiklabs can meet you where you are with hands-on training in your own words. Lab manuals are now available in multiple languages, including Japanese, Brazilian Portuguese, Spanish, and more.
To see which languages are available, click the globe icon by the lab title. You will see all the languages available for that lab.
You will notice that only some labs are available in multiple languages, and in some cases you may not see the language you’re looking for. Adding new languages and translating more labs is an ongoing project, so you can expect to see more over the coming weeks and months.
Don’t see the language you’re looking for? Let us know at Support@Qwiklabs.com!
There’s lots of buzz about Machine Learning and what it can do: on land, at sea, in hospitals, on the chessboard, even extraterrestrial applications. How can you take advantage of ML for your project, here on planet earth? To get the most out of Google Cloud, you want to know how to use ML tools. Not just a high-level understanding of what ML is (though that’s helpful too) – you want to know how ML can help you meet your goals.
And there’s a lab for that! The new 7-lab Machine Learning APIs Quest walks you through use cases and scenarios with Google ML tools. Use these labs to help you understand what ML can do for you. Then, try using ML for your own projects.
Here are some of the labs in the new Quest:
Cloud ML Engine: Qwik start – Train a TensorFlow model to predict income category of a person using the United States Census Income Dataset, both locally and on Cloud ML Engine. To build your model, you’ll use TensorFlow’s prebuilt DNNCombinedLinearClassifier Estimator (this model is sometimes called ‘Wide & Deep’).
AI Chatbot: OK Google! I want to change my password – Build an application to submit tickets with a network and 3 subnetworks that you will use throughout the lab.