Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models.
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This course covers a lot about the data pre-processing, and the tools available in Google Cloud to enable the gruelling tasks. Thanks very much for the lectures and training labs. Very informative.
It's a pretty interesting course, specially that's the only one that teaches featuring engineering with a focus on production issues, but it assumes some knowledge with apache beam, and dataflow.
Great topics, the instructions are great. The only suggestion is cut down the number of different videos. just combine them together. 15 3 mins is not a great experience vs 3 15 min videos.
Some of the labs had big query errors, and some of the google cloud interfaces changed, so careful when doing the labs, the options and the buttons may have been shifted or renamed
關於 Machine Learning with TensorFlow on Google Cloud Platform 專項課程
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.