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Instructor-led Classroom Adult Training in Singapore - Modular Fast Track Skill-Based Trainings

Deep Learning with Tensorflow and Python (CITREP+)

Tensorflow is the most popular and powerful open source machine learning/deep learning framework developed by Google for everyone. Tensorflow has many powerful Machine Learning API such as Neural Network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Word Embedding, Seq2Seq, Generative Adversarial Networks (GAN), Reinforcement Learning, and Meta Learning.

Tensorflow is based on the Python, the most popular programming language for data analytics and engineering in the world. In this course, you will equip yourself the basic and advanced knowledge of Python. After that, you will learn the basic and advanced topics in Tensorflow. By the completion of this course, you will be able to develop your own NN, CNN and RNN model for image recognition and sentimental analysis using either Tensorflow or Keras.

This four day course will teach you the fundamental of Python and follow by Machine Learning with Tensorflow

This course will teach with the latest Tensorflow 2.x

Course Highlights

  • Python programming
  • Machine Learning with Deep NN
  • Image Recognition using Convolutional NN
  • Transfer Leanring with Pretrained Models
  • Sentimental Analysis using Recurrent NN

Learning Outcomes

By the end of the training, learners will be able to:

  • Coding in Python
  • Understand the basic programming concepts such as control structure, looo, function, OOP
  • Understand the basic concepts of Machine Learning and Deep Learning
  • Understand different types of neural networks such as Convolutional Neural Network and Recurrent Neural Network
  • Use Tensorflow and Keras to program Deep Learning applications such as image recognition and sentimental analysis 

Target Audience

  • NSF
  • Full Time Students
  • Data Analysts

IMDA CITREP+ 70%-100% Funding

IMDA CITREP+ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is from 70% -100% . For more information on IMDA CITREP+ funding, click the link below

IMDA CITREP+ Funding Support

Please fill up the IMDA CITREP+ eligibility form after you have registered for this course

CITREP+ Trainee Eligibility Form

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Course Code: CIT005855

Course Booking

$1,200.00 (GST-exclusive)
$1,284.00(GST-inclusive)

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


Course Details

Day 1
Module 1 Basic Python

Topic 1.1 Get Started with Python

  • Overview
  • Install Python
  • Install Sublime Text & PyCharm
  • First Python Script
  • Comment

Topic 1.2 Data Types

  • Number 
  • String 
  • List
  • Tuple
  • Dictionary
  • Set

Topic 1.3 Operators

  • Arithmetic Operators
  • Compound Operators
  • Comparison Operators
  • Membership Operators
  • Logical Operators
  • Identity Operators

Topic 1.4 Control Structure

  • Conditional
  • Loop
  • Iterating Over Multiple Sequences
  • Break & Continue
  • Loop with Else

Topic 1.5 Function

  • Function Syntax
  • Return Single Value
  • Return Multiple Values
  • Passing Arguments
  • Default Arguments
  • Variable Arguments
  • Decorator
  • Lambda, Map, Filter

Topic 1.6 Modules & Packages

  • Modules
  • Packages
  • Python Standard Libraries
  • Install Third Party Packages
  • Anaconda Packages

Day 2
Module 2 Advanced Python

Topic 2.1 Comprehensions & Generators

  • Comprehension Syntax
  • Types of Comprehension
  • Generator Syntax
  • Types of Generators

Topic 2.2 File and Directory Handling

  • Read and Write Data to Files
  • Manage File and Folders with Python OS Module
  • Manage Paths with Python Pathlib Module

Topic 2.3 Object Oriented Programming

  • Introduction to Object Oriented Programming
  • Create Class and Objects
  • Method and Overloading
  • Initializer & Destructor
  • Inheritance
  • Polymorphism

Topic 2.4 Database

  • Setup SQLite3 database
  • Apply CRUD operations on SQLite3
  • Integrate to external databases

Topic 2.5 Error Handling Using Exception

  • Exceptions versus Syntax Errors
  • Handle Exceptions with Try and Except blocks
  • The Else clause
  • Clean up with Finally

Topic 2.6 Intro to Useful Packages

  • Numpy
  • Matplotlib
  • Pandas

Python Assessment

Day 3
Module 3 Basic Tensorflow

Topic 3.1 Overview of Machine Learning & Tensorflow

  • Overview of Machine Learning and Deep Learning
  • Introduction to Tensorflow 2.x
  • Install Tensorflow 2.x

Topic 3.2 Basic Tensorflow Operations

  • Basic Tensor Data Types
  • Constant, Variable & Gradient
  • Matrix Operations
  • Eagle Mode vs Graph Mode

Topic 3.3 Datasets

  • MNIST Handwritten Digits and Fashion Datasets
  • CIFAR Image Dataset
  • IMDB Text Dataset

Topic 3.4 Neural Network for Regression

  • Introduction to Neural Network (NN)
  • Activation Function
  • Loss Function and Optimizer
  • Machine Learning Methodology
  • Build a NN Predictive Regression Model
  • Load and Save Model

Topic 3.5 Neural Network for Classification

  • Softmax
  • Cross Entropy Loss Function
  • Build a NN Classification Model

Day 4
Module 4 Advanced Tensorflow

Topic 4.1 Convolutional Neural Network (CNN)

  • Introduction to Convolutional Neural Network (CNN)
  • Convolution & Pooling
  • Build a CNN Model for Image Recognition
  • Overfitting and Underfitting Issues
  • Methods to Solve Overfitting
  • Small Dataset Overfitting Issue
  • Data Augmentation & Dropout

Topic 4.2 Recurrent Neural Network (RNN)

  • Introduction to Recurrent Neural Network (RNN)
  • Types of RNN Architectures
  • LSTM and GRU
  • Word Embedding
  • Build a RNN Model for Text Classification

Topic 4.3 Transfer Learning & Tensorflow Hub

  • Introduction to Transfer Learning
  • Pre-trained Models
  • Tensorflow Hub
  • Transfer Learning for Feature Extraction & Fine Tuning

Tensorflow Assessment

Course Admin

Prerequisite

The learner must meet the minimum requirement below :

  • Read, write, speak and understand English

Target Audience

  • NSF
  • Full Time Students
  • Data Analysts

Software Requirement

This course will use Google Colab for training. Please ensure you have a Google account.

Funding Validity Period

Valid from 13 May 2019 to 31 Mar 2021

Mode of Training

Instructor-led Classroom Training

CITREP+ Claim Procedure

Trainees who wish to claim for CITREP+ funding must submit their online claim applications to IMDA via ICMS upon course or certification completion. Please refer to the Claim Application Guide for detailed application procedures.

For Organisation-Sponsored Trainees, the claim application will be submitted by the sponsoring organisation.

For Self-Sponsored Trainees, the claim application has to be completed by the individual.

All claims for CITREP+ disbursement must be submitted to IMDA within three (3) months from completion date of the last examination or final post-training assessment. Late submissions will not be accepted. Applications with incomplete supporting documents will be rejected for processing.

CITREP+ Funding Support

Category Type Training course and certification
Organisation- sponsored Non SMEs Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
SMEs Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above)
Self-Sponsored Professionals Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above) Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Students and/or Full-Time National Service (NSF) Up to 100% of the nett payable course and certification fees, capped at $2,500 per trainee

CITREP+ Forms

Who Should Attend

  • NSF or Full Time Students
  • Data Analysts
  • Machine Learning Engineers and Developers

Trainers

Tensorflow TrainerDr. Alfred Ang is the founder of Tertiary Courses. He is a serial entrepreneur. He founded OSWeb2Design Singapore Pte Ltd in 2007 offering web development, e-commerce store development, graphics design, ebook publishing, mobile apps development, and digital marketing services. He established the first online gardening store in Singapore, Eco City Hydroponics Pte Ltd in 2000, offering a wide range of gardening products such as seeds, plant nutrients, hydroponics kits etc. Eco City Hydroponics has become the most popular and successful gardening store in Singapore. He founded Tertiary Infotech Pte Ltd in 2012 and transformed the business to a training platform, Tertiary Courses in 2014. Tertiary Courses offers a wide range of SkillsFuture courses for PMETs to upgrade their skills and knowledge. He also established Tertiary Courses Malaysia in 2016. He also founded Tertiary Robotics in 2015 offering Arduino, Raspberry Pi, Microbit and Robotics products

Dr. Alfred Ang earned his Ph.D. from National University of Singapore in 2000, majoring in Electrical and Electronics Engineering. He also completed an online MBA course with U21 Global based in Australia. He obtained his B.Sc (Hons) from National University of Singapore in 1992, majoring in Physics. He topped his Physics cohort for 3 consecutive years and funded his degree study with Book prizes, Study awards, bursaries and tuition. He has worked in Defence, Electronics and Semiconductor Industries. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.

Dr. Alfred Ang was Distinguished Toastmasters (DTM) and Senior Member of IEEE. He has published more than 20 peer reviewed papers and co-inventors for more than 20 inventions.

Tensorflow Machine LearningDr. Sudipta Samanta received his Ph.D. for his research in the field of computational Biophysics in 2009. He has more than 9 years research experience as a scientist. His current interests include Machine Learning, Deep Learning, Health Care Data analysis, and computer simulation. He is also the founder of "Baby Lion Organic medicinal Farm".

Tensorflow TrainerWee Chee Hong is a veteran consultant in the field of Engineering Design. He has over 20 years experience in electronics/ IT industry with several MNCs including Compaq, Adaptec, Agilent, etc., with specialization in Automation System Design (drilling/ milling machine, 3D machine automation, machine data collection, test & measurement electronics design), Motion control design & applications. With wide technical skill set in development language/ tools .Net, Java, PowerBuilder to C++/ C/C#, he has also strong knowledge in development of firmware for microcontroller such as PIC, ARM and others.

Chee Hong graduated with a B.Eng degree in Electrical Electronics Engineering and holds a Master Degree of Technology from NUS.

Python TrainerTu Anqi is a scholar and dean’s lister with double degree in Business and Computer Science at NTU. Her areas of expertise lies in Python, R Programming, MySQL, Keras, Numpy, Pandas and Matplotlib. She has been working as a Data Analyst at ViSenze Pte Ltd, a visual search and recognition startup. Having strong interests in computer vision, data analytics and machine learning, she has competed and won the top rankings in several data competitions and hackathons. As the current Technical Director of NTU Open Source Society, she conducts regular workshops related to data analytics and data science

Python TrainerClarence Castillo received his bachelors’s degree in Business and Computer Science from Nanyang Technological University. During his undergrad studies at NTU, he served the student community as the Vice President of NTU Open Source Society where he conducted workshops on introductory and advanced topics related to emerging technologies. Prior to receiving his degree, he obtained his diploma in Information Technology from Republic Polytechnic where he graduated top of his cohort along with IMDA Gold Medal award. During his polytechnic studies, he acquired his Java SE7 Oracle Associate certification with a score of 97%. His areas of expertise include languages like Python, JavaScript, Java and TypeScript; along with frameworks such as Angular, Ionic, Express, Spring Boot and Flask. In addition, he also specialises in essential software engineering tools such as version control systems using Git and working with databases like Neo4J, MongoDB and MySQL.

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