Certified Deep Learning Specialist (CDLS) Course

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

CITREP+ course fee support up to 90% (T&Cs apply)

ONLY Eligible Singaporeans who fits the criteria as a Student/Full-Time
NSF/Just ORD can apply for funding support up to 100% (T&C applies).
For more details, please visit go.gov.sg/tesacitrep

CITREP+ course fee support up to 90% (T&Cs apply)

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

ONLY Eligible Singaporeans who fits the criteria as a Student/Full-Time
NSF/Just ORD can apply for funding support up to 100% (T&C applies).
For more details, please visit go.gov.sg/tesacitrep

Course Description

The Certified Deep Learning Specialist (CDLS) course is aimed at data scientists to build complex models with human-like intelligence using Machine Learning and Deep Learning techniques. It is designed to provide aspiring data scientists with an overview of concepts, techniques, and algorithms of machine learning using the Python programming language. End-to-end Machine Learning techniques that include various sampling methods will be addressed, ensemble methods such as the Random Forest and variants of Artificial Neural Network (ANN) such as the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network will be discussed.

Course Objective

The objective of the Certified Deep Learning Specialist (CDLS) course is to enable participants to leverage intuitive approach to build complex models with humans-like intelligence that solves real-world problems using Machine Learning and Deep Learning techniques. In this training, participants will gain a solid understanding and practical skillset of building intelligent computing models using Python Jupyter Notebook platform using packages like Scikit-Learn and TensorFlow.

Skills Acquired

Participants will acquire the skills to develop an end-to-end machine learning model using the Python programming language and model selection method using various open-source Machine Learning tools such as Jupyter notebook, scikit-learn and TensorFlow. Participants will acquire a deep understanding on the theories of artificial neural network, deep learning and examples such as restricted Boltzmann machine and Deep Belief Network (Deep Belief Net).

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

Software used: Jupyter Notebook, Spyder IDE, Anaconda Suite

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

Duration

Pre-Requisites

Funding

Find out more

What our participants say...

“As a Data Scientist, it was a good experience for me in attending the CMLS (Machine Learning) program by GICT as it provided comprehensive...Read more

Dr Ni Wang Dong

Data Scientist, CrimsonLogic Pte Ltd

"I took on the CMLS certified course under GICT recently. I am very impressed with the clear instructions, step-by-step guide, patience instructor...Read more

Zheng Hui Lin

Education Liaison Manager

"The CMLS (Machine Learning) course has covered a broad scope of several machine learning concepts relevant to the real world today. It provided the...Read more

Justin Tan

Singapore Management University (student)