What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
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.
In this course, students learn how to:
√ Think strategically and analytically about ML as a business process and consider the fairness implications with respect to ML
√ How ML optimization works and how various hyperparameters affect models during optimization
√ How to write models in TensorFlow using both pre-made estimators as well as custom ones and train them locally or in Cloud AI Platform
√ Why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep
How Google Does Machine Learning
Launching into Machine Learning
Intro to TensorFlow
The Art and Science of ML
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.
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