Welcome to the Introduction to Google Cloud Machine Learning Engine. I’m Guy Hummel, and I’ll be showing you how to build and run neural networks on the Google Cloud platform. If you look in Google’s documentation for Cloud Machine Learning Engine, you’ll find a Getting Started guide. It gives a walkthrough of the various things you can do with ML Engine, but it says that you should already have experience with Machine Learning and TensorFlow first. Those are two very advanced subjects, which normally take a long time to learn. But I’m going to give you enough of an overview, that you’ll be able to walk through the Getting Started guide with these. To get the most from this course, you should have some experience writing Python code. This is a hands-on course with lots of demonstrations. The best way to learn is by doing, so I recommend that you try performing these tasks yourself on your own Google Cloud account. If you don’t have one, then you can sign up for a free trial. To train your first neural network, we’ll start by going over Machine Learning concepts. Then we’ll go through a TensorFlow program and run it. TensorFlow is a set of Python libraries that make it easier to create neural networks. Google open-sourced it in 2015. Next you’ll learn about deep neural networks, also known as deep learning, and then use Google’s ML Engine to train your Machine Learning model. To see how to improve the accuracy of models, we use feature engineering and then combine two different types of models. After that, we’ll scale up by training a model using a distributed cluster on ML Engine and then deploy the trained model so we can use it to make predictions. If you’re ready to learn how to train a Machine Learning model on the Google Cloud platform, then let’s get started.