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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

Register Now More Info
SkillsFuture Course in Singapore

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

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

Register Now

Objective of the Deep Learning Specialist Course

This deep learning Specialist Course 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. Participants will get a solid understanding of building intelligent computing models in Python Jupiter Notebook platform using Scikit Learn and Tensor Flow after the deep learning training. In addition, they will also appreciate that deep learning is showing promise in areas where the traditional artificial intelligence approaches have failed in the past.

Deep Learning Specialist Course

Why Deep Learning Course ?

Many international firms are engaged in the development of technologies categorized under the field of artificial intelligence (AI). The systems equipped with AI 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 AI have been aiding in the enhancement of the existing computing systems to produce high-value prediction

The global AI 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.

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: Jupyter Notebook, TensorFlow, scikit-learn

Course Outcome
  • Acquire knowledge of the different model selection methods in Machine Learning
  • Acquire knowledge about different types of Deep Neural Networks (MLP, CNN, RNN, LSTM)
  • Design and build Machine Learning models in Python Jupyter notebooks using Scikit Learn framework
  • Deep understanding of the overview of Machine Learning techniques
  • Design and build an end to end model using TensorFlow in Python Jupyter notebook
  • Get hands-on experience in Jupyter Notebook, TensorFlow, scikit-learn
  • 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.
  • SkillsFuture
  • NTUC Training Fund (SEPs)
  • IBF FTS Funding Support


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