Practical Deep Learning with Keras and Python [Video]

More Information
  • Basics of machine learning with minimal math
  • A specialized but optional math-heavy discussion that explains all the inner working of machine learning and deep learning 
  • Applying machine learning principles to solve a real-world case study that includes pre-processing and getting your data into the proper shape. (This case study comes from real research work I have carried out recently.)
  • Understand the often problematic shape issue that makes machine learning difficult to apply in real life 
  • Learn the details of ConvNets and graph-based machine learning models such as Residual Connections and Google's Inception Module 
  • Use Keras' functional API to create powerful models that will help you move way beyond the contents covered in this course 
  • Learn how to use Google's GPUs to speed up your experiments for free
  • Tips on avoiding mistakes made by newcomers to the field and best practices to get you to your goal with minimal effort

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have tried to use a machine learning course but could never figure out how to use it to solve your own problems.
In this course, we will start from scratch. So we will immediately start coding even before installation! You will see a brief bit of absolutely essential theory and then we will get into environment setup and explain almost all concepts through code. You will be using Keras, one of the easiest and most powerful machine learning tools out there.

You will start with a basic model of how machines learn and then move on to higher models, such as:

  • Convolutional Neural Networks 
  • Residual connections 
  • Google's Inception Module

All this with only a few lines of code. All the examples used in the course come with starter code which will get you started and without the hard work.

All the code files are placed at

Style and Approach

This course is based on a case study-based approach and explains why we need machine learning and how everything fits together.

  • Run deep learning models with Keras on a TensorFlow backend
  • Understand how to feed your own data to deep learning models (that is, handling the notorious shape mismatch issue)
  • Understand Deep Learning with minimal math
  • Understand and code Convolutional Neural Networks as well as graph-based deep models involving residual connections and inception modules
  • Understand and use Keras' functional API to create models with multiple inputs and outputs
Course Length 3 hours 26 minutes
ISBN 9781838554729
Date Of Publication 18 Dec 2018


Dr. Mohammad Abdur Razzaque

Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).