Launching into Machine Learning
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.
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
Syllabus
Syllabus - What you will learn from this course
Week 1
Introduction
Get to Know Your Data: Improve Data through Exploratory Data Analysis
Machine Learning in Practice
Week 2
Training AutoML Models Using Vertex AI
BigQuery Machine Learning: Develop ML Models Where Your Data Lives
Week 3
Optimization
Generalization and Sampling
Summary
FAQ
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.
Reviews
Overall it was great, and very instructive. However, the Short History of ML was a little bit confusing with too many unexplained words and too many details too early.
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.
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.
This course gave me a good overview of how to work with GCP for ML and also helped in covering a bit of knowledge gaps that I had when I learnt things on my own.
