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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 24th April 2024

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. 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 Ph.D. in Machine Learning from The University of the West Indies. He moved to London after his Ph.D. to pursue a Computer Vision Researcher position in the industry. His Ph.D. 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.

Our Partners

We are proud to partner with the leading In2scienceUK as a step towards achieving our mission of imparting quality education and knowledge across the globe.

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Usually, there are no general prerequisites when enrolling for any individual courses on However, some courses are certainly more advanced than others that are of introductory and intermediate level. For advanced-level courses, prior knowledge on the topic will be helpful for understanding complex concepts but not mandatory.

The relevant information about the suggested knowledge and background that might be helpful to follow each course is provided on the respective course registration page.

Yes, the courses are flexible and self-paced to suit your schedule. The term “self-paced” implies that the courses do not follow a pre-assigned schedule for learning within the course duration. You will be able to access all the course materials for each course that you have enrolled in as soon as the course begins.

The course completion certificate is CPD accredited. For more informations please visit