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.
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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
Приобретаемые навыки
- Tensorflow
- Bigquery
- Machine Learning
- Data Cleansing
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Попробуйте Coursera для бизнесаот партнера
Программа курса: что вы изучите
Introduction
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
Optimization
Generalization and Sampling
Summary
Рецензии
- 5 stars69,42 %
- 4 stars23,73 %
- 3 stars4,99 %
- 2 stars1,20 %
- 1 star0,63 %
Лучшие отзывы о курсе LAUNCHING INTO MACHINE LEARNING
I got a whole idea on how to work on data from scratch. Model selection, generalization, splitting of data and performance metric were few things I learned from this course.
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.
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.
I highly recommend this course to learners who need an exposure on handling huge datasets using google big query SQL and data splitting strategies.
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