Overview/Description This 6-video course focuses on understanding Google's TensorFlow estimators, and showing learners how they simplify the task of building simple linear and logistic regression models for machine learning solutions. As a prerequisite, learners should have a basic understanding of ML (machine learning), and basic experience programming in Python. Though not required, familiarity with the Scikit-learn library and the Keras API will simplify the labs part of this course. First, you will learn how TensorFlow estimators abstract many of the details in creating a neural network, and you will then learn that you no longer need to define the type of neural network model, nor will you need to add definitions to layer. When using an estimator, learners only need to feed in training and validation data. In the course labs, you will build both a linear regression model and a classifier by using TensorFlow estimators. Finally, you will learn how to evaluate your model using the prebuilt methods available in the estimator.