Embark on a journey through the world of machine learning in French with Google Cloud's "Launching into Machine Learning en Français" course. Explore the nuances of data quality enhancement and exploratory analysis, and dive into the creation, training, and deployment of machine learning models without writing code using Vertex AI AutoML. Discover the advantages of BigQuery ML and learn how to optimize and evaluate models using loss functions and performance metrics. Plus, gain insights into creating reproducible and scalable training, evaluation, and test datasets.
Throughout the course, you will delve into various modules, each focusing on essential aspects of machine learning. From conducting exploratory analysis to practicing machine learning techniques and optimizing models, this course equips you with practical knowledge and hands-on experience to excel in the field of machine learning.
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The course encompasses modules covering data quality enhancement, exploratory analysis, AutoML models with Vertex AI and BigQuery ML, model optimization, and reproducible dataset creation, providing a comprehensive understanding of machine learning.
Embark on your machine learning journey with a comprehensive course introduction, setting the stage for your exploration of crucial concepts and techniques in the field.
Delve into the intricacies of data quality enhancement and exploratory analysis, learning how to leverage Python, BigQuery, and more to enhance the quality of your datasets.
Immerse yourself in the practical application of machine learning techniques, including supervised learning, linear regression, and logistic regression.
Explore the capabilities of Vertex AI for AutoML model training, evaluation, and deployment, gaining hands-on experience with structured data classification models.
Discover the power of BigQuery ML for developing machine learning models within your data storage space, and delve into tuning hyperparameters and creating recommendation systems.
Master the art of optimizing machine learning models, understanding loss functions, gradient descent, performance metrics, and utilizing TensorFlow Playground for advanced training.
Uncover the importance of generalization and sampling in machine learning, learning when to stop model training, creating reproducible datasets, and understanding the nuances of model evaluation.
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