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.
提供方
课程信息
您将学到的内容有
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
您将获得的技能
- Tensorflow
- Bigquery
- Machine Learning
- Data Cleansing
提供方

Google 云端平台
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
授课大纲 - 您将从这门课程中学到什么
Introduction
This module provides an overview of the course and its objectives.
Get to Know Your Data: Improve Data through Exploratory Data Analysis
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.
Machine Learning in Practice
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.
Training AutoML Models Using Vertex AI
In this module, we will introduce training AutoML Models using Vertex AI.
BigQuery Machine Learning: Develop ML Models Where Your Data Lives
In this module, we will introduce BigQuery ML and its capabilities.
Optimization
In this module we will walk you through how to optimize your ML models.
Generalization and Sampling
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.
Summary
This module is a summary of the Launching into Machine Learning course
审阅
- 5 stars69.34%
- 4 stars23.79%
- 3 stars5.01%
- 2 stars1.19%
- 1 star0.64%
来自LAUNCHING INTO MACHINE LEARNING的热门评论
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.
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.
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!
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.
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