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.
About this Course
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
- Machine Learning
- Data Cleansing
Syllabus - What you will learn from this course
Get to Know Your Data: Improve Data through Exploratory Data Analysis
Machine Learning in Practice
Training AutoML Models Using Vertex AI
BigQuery Machine Learning: Develop ML Models Where Your Data Lives
Generalization and Sampling
- 5 stars69.38%
- 4 stars23.73%
- 3 stars5.01%
- 2 stars1.20%
- 1 star0.66%
TOP REVIEWS FROM LAUNCHING INTO MACHINE LEARNING
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.
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!
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.
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.
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