Deep Learning CNN: Convolutional Neural Networks with Python

Preview this course

The course is crafted to reflect the in-demand skills in the marketplace that will help you in mastering the concepts and methodology of CNNs in data science with regard to Python. The course helps you apply state-of-the-art CNNs that are much more recent, advanced in terms of accuracy and efficiency, and can be used for transfer learning on your own dataset.

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159 on-demand videos & exercises
Level: Beginner
15hrs 26mins
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What to know about this course

Convolutional Neural Networks (CNNs) are considered game-changers in the field of computer vision, particularly after AlexNet in 2012. They are everywhere now, ranging from audio processing to more advanced reinforcement learning. So, the understanding of CNNs becomes almost inevitable in all fields of data science. With this course, you can take your career to the next level with an expert grip on the concepts and implementations of CNNs in data science.

The course starts with introducing and jotting down the importance of Convolutional Neural Networks (CNNs) in data science. You will then look at some classical computer vision techniques such as image processing and object detection. It will be followed by deep neural networks with topics such as perceptron and multi-layered perceptron. Then, you will move ahead with learning in-depth about CNNs. You will first look at the architecture of a CNN, then gradient descent in CNN, get introduced to TensorFlow, classical CNNs, transfer learning, and a case study with YOLO. Finally, you will work on two projects: Neural Style Transfer (using TensorFlow-hub) and Face Verification (using VGGFace2).

By the end of this course, you will have understood the methodology of CNNs with data science using real datasets. Apart from this, you will easily be able to relate the concepts and theories in computer vision with CNNs. All the resource files are added to the GitHub repository at:

Who's this course for?

This course is designed for beginners in data science and deep learning. Any individual who wants to learn CNNs with real datasets in data science, learn CNNs along with its implementation in realistic projects, and master their data speak will gain a lot from this course.

No prior knowledge is needed. You start from the basics and slowly build your knowledge of the subject. A willingness to learn and practice is just the prerequisite for this course.

What you'll learn

  • Understand the importance of CNNs in data science
  • Explore the reasons to shift from classical computer vision to CNNs
  • Learn concepts from the beginning with comprehensive unfolding with examples in Python
  • Study the evolutions of CNNs from LeNet (1990s) to MobileNets (2020s)
  • Deep-dive into CNNs with examples of training CNNs from scratch
  • Build your own applications for human face verification and neural style transfer

Key Features

  • Learn from easy-to-understand, exhaustive, expressive, 75+ videos along with detailed code notebooks
  • Structured course with solid basic understanding and moving ahead with the advanced practical concepts
  • Practical explanation and live coding with Python to build your own application

Course Curriculum

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science.
They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

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