This training focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This training picks up where “Machine Learning on Google Cloud Platform” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a module on building recommendation systems.
- Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
- Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving.
- Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning.
- Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs.
- Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models.
- Implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow.
- Data Engineers and programmers interested in learning how to apply machine learning in practice.
- Anyone interested in learning how to leverage machine learning in their enterprise.
To get the most out of this training, participants should have:
- Knowledge of machine learning and TensorFlow to the level covered in Machine Learning on Google Cloud Platform coursework.
- Experience coding in Python.
- Knowledge of basic statistics.
- Knowledge of SQL and cloud computing (helpful).
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