Online Advanced Machine Learning with TensorFlow on Google Cloud Platform

This series of courses teaches participants the following skills:

• 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 build and operationalize TensorFlow models

To get the most out of this specialization, participants should have:

• Knowledge of machine learning and TensorFlow to the level of Machine Learning on GCP specialization
• Experience coding in Python
• Knowledge of basic statistics
• Knowledge of SQL and cloud computing (helpful)