GICT Certified Deep Learning Specialist (CDLS) course


Certification in
AI/ML, Big Data, Cloud Computing & IoT

Up to 90% funding for SMEs and professionals (conditions apply)
Special course fee for Tertiary Student/NSF

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Objective of the Deep Learning Specialist Course

GICT Certified Deep Learning Specialist course (CDLS) will focus on the implementation of one of the newest libraries for implementing Deep Learning, called the Tensor Flow. This certification leverages intuitive approach to build complex models with humans like intelligence that will help to solve real-world problems using Machine Learning and Deep Learning techniques.

Deep Learning Specialist Course

Many international firms are engaged in the development of technologies categorized under the field of Artificial Intelligence. The systems equipped with Artificial Intelligence do not require constant human intervention to carry out the designated process. Such an intelligent computing system has the ability to learn things on its own according to the situation.

Technologies such as deep learning, intelligent robots and neuro-linguistic programming under Artificial Intelligence have been aiding in the enhancement of the existing computing systems to produce high-value prediction.

The global artificial intelligence market is expected to reach USD 35,870.0 million by 2025 from its direct revenue sources, growing at a CAGR of 57.2% from 2017 to 2025. And the Deep Learning market, in particular, is expected to be worth USD 1772.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. The Asia Pacific regional market is expected to be the fastest-growing market for Deep Learning, owing to the improvements in information storage capacity, high computing power, and parallel processing. The major drivers for the growth of the Deep Learning market are various industry verticals such as advertisement, finance and automotive. 

After the course, participants will have a good understanding to build intelligent computing models in Python Jupiter Notebook platform using Scikit Learn and Tensor Flow. Participants will appreciate that Deep Learning is showing promise in areas where the traditional Artificial Intelligence approaches have failed in the past.

Course Outline
  • Unit 1 : Machine Learning – An overview
  • Unit 2 : Model Selection methods in Machine Learning
  • Unit 3 : Libraries for scientific computation and data analysis
  • Unit 4 : Artificial Neural Network
  • Unit 5 : Theories under Deep Learning
  • Unit 6 : Types of Neural Network
  • Unit 7 : Introduction to TensorFlow
  • Unit 8 : Restricted Boltzman Machine and DeepBeliefNet

Tools/Software used: Project Jupyter notebook, TensorFlow, scikit-learn

Course Outcome
  • Understand the overview of Machine Learning techniques
  • Understand the different model selection methods in Machine Learning
  • Understand the basic types of Deep Neural Networks (MLP, CNN, RNN, LSTM)
  • Design and build Machine Learning models in Python Jupyter notebooks using Scikit Learn framework
  • Design and build an end to end model using Tensor Flow in Python Jupyter notebooks
  • 32 Hours (4 Days) Classroom Training
  • Participants are recommended to have preferably 1 year of experience in software development, business domain or data/business analysis. However, if you do not have any experiences, you can still consider taking up the course and we will advise / assist you accordingly.


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