How Google does Machine Learning
What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to feature
Created by: Google Cloud Training
Quality Score
Overall Score : 96 / 100
Course Description
Instructor Details
- 4.8 Rating
499 Reviews
Google Cloud Training
The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps millions of organizations empower their employees, serve their customers, and build what's next for their businesses with innovative technology created in-and for-the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
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Reviews
By Francesco R on 15-Nov-19
The course provided an interesting overview of the strategic aspects of a ML application and an introduction to Google tools for ML and data analysis. I would have liked a more hands-on approach but I have to admit that the contents were nicely presented and very interesting.
By Mai A D on 15-Nov-19
Very practical and enjoyable to follow.
By Sai P R G on 29-Oct-19
Very interesting course. Thank you coursera and qwiklabs for providing this beautiful course about "How google does machine learning"
By Ryan M on 28-Oct-19
Trying to run the Code from a local machine was a little more of a challenge than I thought it would be. Here's a code snippet that hopefully will help others in the future when they attempt to do the same.#For this to work change __init__ in Programs\Python\Python37\Lib\site-packages\httplib2 line 1090 from int to float
APIKEY="AIzaSyD4EXe8wuo38HlDLYcmr9zUT5JBXo7uY00"
from googleapiclient.discovery import build
service = build('translate', 'v2', developerKey=APIKEY)
inputs = ['I love words that aren\'t']
outputs = service.translations().list(source='en', target='fr', q=inputs).execute()
for input, output in zip(inputs, outputs['translations']):
print("{0} -> {1}".format(input, output['translatedText']))
By KimYongWan on 20-Oct-19
Very good understandable and easy to use something toolsPerfect
By Bogook C on 20-Oct-19
It was very meaningful experience to know how google is doing with lots of data which google has.
By 정희철 on 18-Oct-19
excellent explanation with details
By Hsin W C on 17-Oct-19
Thank you for this fun and playful course you make my day become brighter:)
By 令安 趙 on 15-Oct-19
Nice introduction to ML and basic GCP!
By Saksham on 6-Oct-19
A great beginner freindly course. There are no confusions at any points, all instructions are easy and clear
By Shubham k S on 5-Oct-19
This course really get me excited, it was so wonderful to see how google is using ML in day to day uses and in their product.
By Paitoon p on 3-Oct-19
Good Course
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