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.
Launching into Machine Learning
Launching into Machine Learning
Instructor: Google Cloud Training
53,608 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.6
(4,327 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 14 hours
Learn at your own pace
90%
Most learners liked this course
What you'll 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'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
6 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 8 modules in this course
This module provides an overview of the course and its objectives.
What's included
1 video
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.
What's included
9 videos1 reading1 assignment2 app items
In this module, we will introduce some of the main types of machine learning so that you can accelerate your growth as an ML practitioner.
What's included
6 videos1 reading1 assignment1 app item
In this module, we will introduce training AutoML Models using Vertex AI.
What's included
5 videos1 reading1 assignment
In this module, we will introduce BigQuery ML and its capabilities.
What's included
7 videos1 reading1 assignment1 app item
In this module we will walk you through how to optimize your ML models.
What's included
12 videos1 reading1 assignment
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.
What's included
5 videos1 reading1 assignment
This module is a summary of the Launching into Machine Learning course
What's included
4 readings
Instructor
Instructor ratings
4.7 (282 ratings)Offered by
Explore more from Machine Learning
Google Cloud
Google Cloud
Amazon Web Services
Amazon Web Services
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
4.6
4,327 reviews
- 5 stars
69.47%
- 4 stars
23.61%
- 3 stars
5.03%
- 2 stars
1.20%
- 1 star
0.67%
Showing 3 of 4327
SS
5
Reviewed on Dec 3, 2018
A great course to boost your confidence on practicing ML. It also teaches you some fresh skills like repeatable dataset partitioning techniques using just SQL.
AK
4
Reviewed on Aug 25, 2018
Very good course for beginners!-1 star because I find labs to be less informational and practical and course to be more theoretical that practical!
AM
4
Reviewed on Jul 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.
New to Machine Learning? Start here.
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
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.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
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.
More questions
Financial aid available,