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Data Science 3: Deep Learning

In this course we continue our journey deeper into machine learning, with a look at popular deep learning techniques. This course focuses heavily on neural networks and the various architectures used to solve problems no other machine learning techniques can solve. From multi-layer perceptrons to convolutional neural networks and recurrent neural networks, we explore the pros and cons of each technique. Each topic provides a theoretical understanding paired with a hands-on exercise to be solved in python and real word data.

Starts 12th August 2022

About This Course

This course provides a deeper look into machine learning techniques. You will be exposed to some of the most popular Python libraries that facilitate the training of various types of neural network architectures. You will learn how to create CNNs for image classification and regression problems. You will learn how to use RNNs and LSTMs to solve problems. You will learn how to apply deep learning techniques to tackle common problems of underfitting and overfitting. An exposure to the world of deep learning research will also be provided.

Who Is This Course For

This course is for anyone with a basic understanding of machine learning, linear algebra, and Python. The knowledge gained in this course can be used to open the gateway to deep learning research. This class certification will be advantageous to anyone seeking a deep learning role in industry or looking to pursue an advanced degree in artificial intelligence.

What you'll learn

  • Fundamental deep learning concepts
  • How to build a CNN for image classification
  • How to build upon transfer learning
  • How to build and train RNNs and LSTMs
  • How to use deep learning techniques to optimise and accelerate training deep models

Meet Your Instructor

Dr. Sterling Ramroach

Sterling has a PhD in Machine Learning from The University of the West Indies. He moved to London after his PhD to pursue a Computer Vision Researcher position in industry. His PhD research focused on accelerating machine learning techniques with parallel processing and applying it to bioinformatics. His work is published in peer-reviewed scientific journals including Molecular Omics, Expert Systems with Application, and BMJ Innovations.

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The courses together with the seminars offered are based on a subscription package. You can choose a six-months and annual subscription. A six-months subscription costs £210/€260/$280, while an annual subscription costs £399/€480/$530. With a six-months or annual subscription, you get unlimited access to all the courses and on-demand seminars offered by CBEHx for the subscription duration. Upon unsubscribing, you will no longer have access to the contents. There is no limit to how many certificates you can earn during your subscription period. To subscribe, please visit the pricing page on

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Once you have subscribed, visit the seminars catalogue on Select the seminar that you want to view and enjoy the learning experience. You can watch each seminar as many times as you want. We keep on adding new seminars, so keep checking the catalogue for new seminar releases!