4.6
stars
4,228 ratings

Google Cloud Training

43,961 already enrolled

Offered By

Google Cloud

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Beginner Level

Approx. 17 hours to complete

English

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What you will learn

Describe how to improve data quality and perform exploratory data analysis
Build and train AutoML Models using Vertex AI and BigQuery ML
Optimize and evaluate models using loss functions and performance metrics
Create repeatable and scalable training, evaluation, and test datasets

Skills you will gain

Tensorflow
Bigquery
Machine Learning
Data Cleansing

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Approx. 17 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.7/5 (260 Ratings)

Google Cloud Training

1,924,077 Learners

1,038 Courses

Offered by

Google Cloud

We help 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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Syllabus - What you will learn from this course

Content Rating90%(3,828 ratings)

Week1

Week 1

1 minute to complete

Introduction

1 minute to complete

1 video (Total 1 min)

4 hours to complete

Get to Know Your Data: Improve Data through Exploratory Data Analysis

In this module, we look at how to improve the quality of our data and how to explore our data by performing exploratory data analysis. We look at the importance of tidy data in Machine Learning and show how it impacts data quality. For example, missing values can skew our results. You will also learn the importance of exploring your data. Once we have the data tidy, you will then perform exploratory data analysis on the dataset.

4 hours to complete

9 videos (Total 57 min), 1 reading, 3 quizzes

9 videos

Introduction39sImprove Data Quality13mGetting Started with Google Cloud and Qwiklabs4mLab intro: Improve the quality of your data1mLab Demo: Improve the quality of your data22mWhat is exploratory data analysis4mHow is EDA used in machine learning4mData analysis and visualization3mLab intro: Explore the data using Python and BigQuery46s

1 reading

Resources: Get to know your data: Improve data through Exploratory Data Analysis10m

1 practice exercise

Get to know your data: Improve data through Exploratory Data Analysis10m

3 hours to complete

Machine Learning in Practice

3 hours to complete

6 videos (Total 45 min), 1 reading, 2 quizzes

Week2

Week 2

3 hours to complete

Training AutoML Models Using Vertex AI

3 hours to complete

7 videos (Total 38 min), 1 reading, 2 quizzes

7 videos

Introduction23sMachine learning vs. deep learning7mWhat is automated machine learning?1mAutoML regression model10m(Optional) Lab intro: Training an AutoML classification model (Structured data)28s(Optional) Lab Demo: Training an AutoML classification model (Structured data)7mEvaluate AutoML models10m

1 reading

Resources: Training AutoML Models Using Vertex AI10m

1 practice exercise

Training AutoML Models Using Vertex AI18m

3 hours to complete

BigQuery Machine Learning: Develop ML Models Where Your Data Lives

3 hours to complete

8 videos (Total 30 min), 1 reading, 3 quizzes

8 videos

Introduction22sTraining an ML model using BigQuery ML5mBigQuery Machine Learning supported models2mLab intro: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)29sLab Demo: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)11mBigQuery ML hyperparameter tuning3m(Optional) Lab intro: Using the BigQuery ML hyperparameter tuning to improve model performance28sHow to build and deploy a recommendation system with BigQuery ML5m

1 reading

Resources: BigQuery Machine Learning: Develop ML Models Where Your Data Lives10m

1 practice exercise

BigQuery Machine Learning: Develop ML Models Where Your Data Lives10m

Week3

Week 3

1 hour to complete

Optimization

1 hour to complete

12 videos (Total 58 min), 1 reading, 1 quiz

12 videos

Introduction26sDefining ML models4mIntroducing the course dataset6mIntroduction to loss functions6mGradient descent5mTroubleshooting loss curves2mML model pitfalls6mLecture lab: Introducing the TensorFlow Playground6mLecture lab: TensorFlow Playground - Advanced3mLecture lab: Practicing with neural networks6mPerformance metrics3mConfusion matrix5m

1 reading

Resources: Optimization10m

1 practice exercise

Optimization10m

1 hour to complete

Generalization and Sampling

Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.

1 hour to complete

5 videos (Total 28 min), 1 reading, 1 quiz

2 hours to complete

Summary

2 hours to complete

4 readings

Reviews

4.6

481 reviews

5 stars
69.38%
4 stars
23.73%
3 stars
5.01%
2 stars
1.20%
1 star
0.66%

TOP REVIEWS FROM LAUNCHING INTO MACHINE LEARNING

by AMJul 13, 2018

Great presenter. High energy engaging. The material is more difficult and to develop intuition of why the sampling needs to result in constant RMSE didn't come across.

by CRAug 12, 2021

Interesting course, and the technical details during week 3 and 4 were highly appreicated. The labs could have been a bit better put together, but all round happy with this course!

by PAAug 3, 2018

Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.

by KTApr 8, 2019

My favourite course in the specialisation. I think it's a great idea to use historic time-frame to explain the advances in ML and why there is so much hype around deep learning.

View all reviews

Frequently Asked Questions

Can I preview a course before enrolling?
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
What will I get when I enroll?
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
When will I receive my Course Certificate?
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
Why can’t I audit this course?
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

More questions? Visit the Learner Help Center.

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What you will learn

Skills you will gain