Join the Machine Learning with TensorFlow on Google Cloud en Español course to delve into the world of machine learning. This comprehensive program guides you through the five phases of transforming a potential practical case into a valuable machine learning resource. From understanding the basics of machine learning to designing and training models using TensorFlow 2.x and Keras, this course provides hands-on experience in building, optimizing, and deploying machine learning models on Google Cloud Platform.
Throughout the course, you'll learn about responsible AI practices, leveraging Vertex AI Platform, and harnessing the power of Google Cloud tools for machine learning. The curriculum covers feature engineering, model generalization, and optimization techniques, ensuring that you acquire the skills to tackle real-world machine learning challenges.
Embark on this journey and gain practical experience in building end-to-end machine learning solutions with Google Cloud. By the end of the course, you'll be equipped to apply your knowledge to solve diverse real-world problems using machine learning.
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Immerse yourself in the world of machine learning with TensorFlow on Google Cloud in Spanish. From understanding the basics to mastering advanced techniques, this course equips you with the skills to build and deploy machine learning models on Google Cloud Platform.
Discover how Google implements machine learning with Vertex AI Platform to create, train, and deploy AutoML models without coding. Learn about best practices for responsible AI and leverage Google Cloud tools for machine learning.
Enhance data quality, perform exploratory data analysis, and compile and train AutoML models using Vertex AI and BigQuery ML. Optimize and evaluate models using loss functions and performance metrics, and create scalable training, evaluation, and test datasets.
Focus on utilizing the flexibility and ease of TensorFlow 2.x and Keras to design, train, and deploy machine learning models. Gain practical experience in working with datasets, designing data input pipelines, and creating deep learning models using Keras's sequential and functional APIs.
Utilize Vertex AI Feature Store, perform feature engineering from raw data, preprocess attributes using Apache Beam and Cloud DataFlow, and employ tf.Transform. This module covers the essential steps from raw data to engineered attributes.
Learn techniques for generalizing machine learning models using regularization, optimize model performance by adjusting batch size and learning rate, and apply these concepts to TensorFlow code. Gain insights into model optimization and practical application of machine learning concepts.
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