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 stars69.39%
- 4 stars23.72%
- 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.
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.
This course is very helpful to understand the machine learning concepts of various modals, splitting of the data and even training the model for benchmark.
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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