Artificial Neural Networks and Deep Learning in Practice

With Introductory Review on Machine Learning

What you'll learn
The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
The fundamental of Deep learning and the most popular algorithms.
It has several coding examples.
It has several quizzes for learning better.

Requirements
Probability,
Calculus,
Basic of Python, Tensor Flow, Keras, and Numpy.

Description
Artificial Neural Networks and Deep Learning are the most recent and advanced topics in machine learning, with several applications in many fields. They show promising results in many areas, from computer vision to drug discovery and stock market prediction. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using Artificial Neural Networks and Deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms using Python, TensorFlow, and Keras.

The course provides some references and links for more reading. You have access to the Q/A session for asking your question. You also have access to communicate with the professor by the messaging system to ask your questions.

I would expect this course’s contents to be welcomed worldwide by undergraduate and graduate students and researchers in deep learning, including practitioners in academia and industry.

Who this course is for
It is useful for undergraduate and graduate students, as well as practitioners in industry and academia.
Anyone who would like to learn Neural networks and deep learning.