This free online course in practical machine learning with TensorFlow will begin by introducing you to the concept of machine learning and the overview of TensorFlow. You will learn about the steps in the machine learning process, logistic regression and the loss unction in machine learning. You will also be introduced to gradient descent, gradient descent variations, machine learning visualization as well as confusion matrix.

The course then introduces the concept of tensors and their relevance. You will learn about the mathematical fundamentals of deep learning. You will also learn how to build data pipelines for TensorFlow as well as text processing with TensorFlow. Next, you will be introduced to machine learning models, text classification, overfitting, underfitting, regression and the architecture of neural network model.

The course then explains the meaning of convolution neural network and transfer learning. You will also learn about the pooling, and image classification and visualization. This course explains in great detail estimator API, boosted trees as well as word embeddings and its application. This course also explains the customization of TensorFlow, writing a custom layer as well as Tensorflow distributed training. This course has not been updated with the use of Generative AI models, like ChatGPT.