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
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.
Just stick with it! The course is easy to follow, and the labs get you into tweaking ML related code without having to know the underlying math. LOVE IT!
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!
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.
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