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Certificate Associate in Data Science – Deep Learning

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Certificate Associate in Data Science – Deep Learning

CADS Deep Learning

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep-learning algorithms are used broadly across different industries. This course will give you all the practical information available on the subject, including best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.

Starting with a quick recap of important machine learning concepts, the course will delve straight into deep learning principles using scikit-learn.

Deep learning is a subset of a more general field of artificial intelligence called machine learning, which is predicated on this idea of learning from example. In machine learning, instead of teaching a computer a massive list of rules to solve the problem, we give it a model with which it can evaluate examples, and a small set of instructions to modify the model when it makes a mistake. We expect that, over time, a well-suited model would be able to solve the problem extremely accurately.

To accommodate this complexity, recent research in machine learning has attempted to build models that resemble the structures utilized by our brains. It’s essentially this body of research, commonly referred to as deep learning, that has had spectacular success in tackling problems in computer vision and natural language processing.

Learning Objectives

Upon completion of the course, participants should be able to:

  • Explain different machine learning approaches and techniques and some of their applications to real-world problems
  • Explain what neural networks
  • Describe how a neuron works and how we can stack many layers to create and use deep feed-forward neural networks
  • Apply auto-encoders and restricted Boltzmann machines
  • Apply convolutional layers
  • Apply Recurrent Neural Networks and Language Models
  • Explain the difference and similarities of concepts between outlier detection and anomaly detection

Who should attend

Data Analysts, Data Engineers, Data Science Enthusiasts, Business Analysts, Project Managers


Foundational certificate in Big Data/Data Science

This course is meant for anyone who are comfortable developing applications in Python, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Python programming language will also find this course to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing python stack, this course is for you

Delivery Method

Mix of Instructor-led, case study driven and hands-on for select phases

H/w, S/w Reqd

Python, Pandas, Numpy, System with at least 2GB RAM and a Windows /Ubuntu/Mac OS X operating system


24 Hours (2 days Instructor led + 8 hours online learning)

Enroll Now
  • Course Name:Certificate Associate in Data Science – Deep Learning
  • Location:Singapore
  • Duration:2 days classroom + 8 hours online
  • Exam Time: 60 minutes
  • Course Price: Call for price
  • Minimum requirements: Foundational Certificate in Programming

 ITPACS LogoITPACS Data Science Certification Road Map

Course contents

# Topic Method of Delivery
Day 1

Introduction to Deep Learning

Distributed feature representation

Hierarchical feature representation

Deep learning with GPU

Deep learning hardware guide

CPU cores

CPU cache size

RAM size

Hard drive

Cooling systems

Instructor Led

Basics of linear algebra

Data representation

Data operations

Matrix properties

Instructor Led

Deep learning software frameworks


Multilayer perceptrons

The input layer

The output layer

Hidden layers

Activation functions

Sigmoid or logistic function

Tanh or hyperbolic tangent function


Leaky ReLU and maxout


Choosing the right activation function

How a network learns

Weight initialization

Forward propagation


Calculating errors


Updating the network

Automatic differentiation

Vanishing and exploding gradients

Optimization algorithms


Instructor Led

Convolutional Neural Networks



Fully connected layer


Instructor Led

Case Study

Hands-on session
Day 2

Restricted Boltzmann Machines

Energy function

Encoding and decoding

Contrastive divergence (CD-k)

Stacked/continuous RBM

RBM versus Boltzmann Machines

Instructor Led

Recurrent neural networks (RNN/LSTM)

Cells in RNN and unrolling

Backpropagation through time

Vanishing gradient and LTSM


NLP – Vector Representation


Computer Vision


Case Study

Hands–on session

Reinforcement learning

Problem setup

Value learning-based algorithms

Policy search-based algorithms

Actor-critic-based algorithms



Online Self paced


  • Certificate Title: Certificate Associate in Data Science – Deep Learning
  • Certificate Awarding Body: ITPACS


Information Technology Professional Accreditations and Certifications Society (ITPACS) is a non-profit organization focused on improving technology skills for the future. ITPACS offers associate level, professional level and leader certifications across 6 domains including data science, web development, mobile development, cyber security, IoT and blockchain. Applicants have to go through a exam eligibility process demonstrating their experience.

Certification Roadmap

CADS Deep Learning Outline


The Associate certification is catered to individuals with less than 1 year working experience in the field. This is ideal for newcomers starting out in the profession or those seeking to make an entry into the profession. Applicants are required to have completed the application process prior to taking the exam.  

Styling Eligibility


  • Exam Format: Closed-book format.
    Questions: 30 multiple choice questions, coding exercises
    Passing Score: 65%
    Exam Duration: 60 minutes
  • Exam needs to be taken within 12 months from the exam voucher issue date

ITPACS Certification Training Road Map

Data Science

Data science is not a single science as much as it is a collection of various scientific disciplines integrated for the purpose of analyzing data. These disciplines include various statistical and mathematical techniques, including:

  • Computer science
  • Data engineering
  • Visualization
  • Domain-specific knowledge and approaches

With the advent of cheaper storage technology, more and more data has been collected and stored permitting previously unfeasible processing and analysis of data. With this analysis came the need for various techniques to make sense of the data. These large sets of data, when used to analyze data and identify trends and patterns, become known as big data.

The process of analyzing big data is not simple and evolves to the specialization of developers who were known as data scientists. Drawing upon a myriad of technologies and expertise, they are able to analyze data to solve problems that previously were either not envisioned or were too difficult to solve.

The various data science techniques that we will illustrate have been used to solve a variety of problems. Many of these techniques are motivated to achieve some economic gain, but they have also been used to solve many pressing social and environmental problems. Problem domains where these techniques have been used include finance, optimizing business processes, understanding customer needs, performing DNA analysis, foiling terrorist plots, and finding relationships between transactions to detect fraud, among many other data-intensive problems.

Data mining is a popular application area for data science. In this activity, large quantities of data are processed and analyzed to glean information about the dataset, to provide meaningful insights, and to develop meaningful conclusions and predictions. It has been used to analyze customer behavior, detecting relationships between what may appear to be unrelated events, and to make predictions about future behavior.

Machine learning is an important aspect of data science. This technique allows the computer to solve various problems without needing to be explicitly programmed. It has been used in self-driving cars, speech recognition, and in web searches. In data mining, the data is extracted and processed. With machine learning, computers use the data to take some sort of action.