Right from explaining the basic understanding of What a machine learning is, the course will take you through a series of concepts in setting up a supervised learning problem, writing distributed machine learning models that scale in Tensorflow, incorporating the right mix of parameters that yields accurate, generalized models and a detailed conceptual knowledge in Tensorflow.
30,187 students have already enrolled for the course.
You must have some concepts in Machine Learning, Deep Learning and in Python & Jupyter notebooks before enrolling for this course. The foundational concepts in TensorFlow that are required by you to learn are provided in the course. You will understand through this course:
different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
Applying TensorFlow for backpropagation
how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions.
how to apply TensorFlow for backpropagation to tune the weights and biases