What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code? When should you use automated machine learning or custom training?
This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker); use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; use Vertex Vizier hyperparameter tuning to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems, write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud.
> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service
Overview
Syllabus
Course 1: How Google does Machine Learning
- Offered by Google Cloud. This course explores what ML is and what problems it can solve. The course also discusses best practices for ... Enroll for free.
Course 2: Launching into Machine Learning
- Offered by Google Cloud. The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. ... Enroll for free.
Course 3: TensorFlow on Google Cloud
- Offered by Google Cloud. This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML ... Enroll for free.
Course 4: Feature Engineering
- Offered by Google Cloud. This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and ... Enroll for free.
Course 5: Machine Learning in the Enterprise
- Offered by Google Cloud. This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML ... Enroll for free.
- Offered by Google Cloud. This course explores what ML is and what problems it can solve. The course also discusses best practices for ... Enroll for free.
Course 2: Launching into Machine Learning
- Offered by Google Cloud. The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. ... Enroll for free.
Course 3: TensorFlow on Google Cloud
- Offered by Google Cloud. This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML ... Enroll for free.
Course 4: Feature Engineering
- Offered by Google Cloud. This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and ... Enroll for free.
Course 5: Machine Learning in the Enterprise
- Offered by Google Cloud. This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML ... Enroll for free.
Courses
-
This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
-
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.
-
This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
-
This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
-
This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
Taught by
Google Cloud Training
Related Courses
-
Preparing for Google Cloud Certification: Machine Learning Engineer
-
Preparing for Google Cloud Certification: Machine Learning Engineer
-
Advanced Machine Learning on Google Cloud
-
Hands-on Machine Learning with Google Cloud Labs
-
Advanced Machine Learning on Google Cloud
-
Machine Learning with TensorFlow on Google Cloud en Français
