Explore the world of machine learning with TensorFlow on Google Cloud in the Japanese language. This comprehensive course delves into the practical application of machine learning, covering a wide range of topics such as identifying use cases for machine learning, leveraging neural networks, implementing responsible AI practices, and creating scalable distributed machine learning models.
With a focus on practical exercises, you will learn how to enhance data quality, optimize and evaluate models using loss functions and performance metrics, and create reproducible and scalable training, evaluation, and test datasets. Furthermore, the course provides hands-on experience in utilizing TensorFlow and Keras to create, train, and deploy machine learning models, incorporating features like data pipeline design, feature engineering, and large-scale model training and deployment on Google Cloud Platform.
The course culminates in teaching the art and science of machine learning, covering model fine-tuning, regularization techniques, hyperparameter influence, and common model optimization algorithms. You will also gain insights into transforming raw data into valuable features, leveraging tools such as Vertex AI Feature Store, Apache Beam, Cloud DataFlow, and tf.Transform.
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Embark on a machine learning journey with Google Cloud's TensorFlow course in Japanese. Covering topics such as using Vertex AI for building and training AutoML models, improving data quality, exploring TensorFlow 2.x and Keras, feature engineering, and fine-tuning machine learning models.
Gain an overview of the Vertex AI platform and learn to build, train, and deploy AutoML machine learning models without writing code. Explore best practices for implementing machine learning on Google Cloud.
Learn to enhance data quality, 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, and create reproducible and scalable datasets.
Delve into the practical use of TensorFlow 2.x and Keras, covering topics such as data pipeline design, feature engineering, creating deep learning models using Keras Sequential API and Functional API, and large-scale model training and deployment on Cloud AI Platform.
Utilize Vertex AI Feature Store, Apache Beam, Cloud DataFlow, and tf.Transform to transition from raw data to valuable features, and learn about feature engineering techniques.
Discover the art and science of machine learning, focusing on model fine-tuning, regularization techniques, hyperparameter influence, and common model optimization algorithms.
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