This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.

We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.