Skills Covered: Deep Learning, PyTorch for Machine Learning, Deep Learning with Python, Deep Learning with PyTorch, Deep Learning with Tensorflow, GPUs to Scale, Speed-up Deep Learning, Neural Networks, Deep Learning Libraries, Deep Learning Models, Prediction, Linear Regression, Gradient Descent, Optimization in PyTorch, Deep Networks, Computer Vision Networks, Dimensionality Reduction, Autoencoders, TensorFlow, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Restricted Boltzmann Machine, Hardware Accelerated Deep Learning, Deep Learning in the Cloud, Distributed Deep Learning,

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AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware.

The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers — and the job salaries of Deep Learning practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.

Within this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision.

Throughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers.

This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.



  • Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
  • Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
  • Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
  • Master Deep Learning at scale with accelerated hardware and GPUs.
  • Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.



  • Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020. (Source: Burning Glass Technologies, Business-Higher Education Forum (BHEF), and IBM)
  • Average salary for a Machine Learning Engineer is $136,054 (Source:
  • Career prospects include Deep Learning & Computer Vision Engineer, Machine Learning Engineer, Data Scientist, Data Analyst, Data Engineer, AI / Deep Learning Scientist and Data Science Instructor

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Deep Learning by IBM
Deep Learning by IBM

£418.00 £376.00

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