Get set for a career as a Deep Learning Engineer with salary starting from RM4,000


16 days Bootcamp is to equip, develop and assist Fresh Graduates and Working Adults with relevant skill, knowledge and experience as required by the industries that can enhance their employability.

Bootcamp Topics:

Candidate will be offered with guaranteed job placement with starting salary RM4,000 upon passing the exam.


MODULE 1: Setting up Python and Developing a Simple Application
• Topic A: Set Up the Development Envinronment
• Topic B: Write Python Statements
• Topic C: Create a Python Application
• Topic D: Prevent Errors


MODULE 2: Processing Simple Data Types
• Topic A: Process Strings and Integers
• Topic B: Process Decimals, Floats and Mixed Number Types


MODULE 3: Processing Data Structures
• Topic A: Process Ordered Data Structures
• Topic B: Process Unordered Data Structures

MODULE 4: Writing Conditional Statements and Loops in Python
• Topic A: Write a Conditional Statement
• Topic B: Write a Loop


MODULE 5: Structuring Code for Reuse
• Topic A: Define and Call a Function
• Topic B: Define and Instantiate a Class
• Topic C: Import and Use a Module


MODULE 6: Writing Code to Process Files and Directories
• Topic A: Write to a Text File
• Topic B: Read from a Text File
• Topic C: Get the Contents of a Directory
• Topic D: Manage Files and Directories


MODULE 7: Dealing with Exceptions
• Topic A: Handle Exceptions
• Topic B: Raise Exceptions

MODULE 1: What is a Java Program
• Key features of the Java language
• Java technology and development environment
• Running and testing a Java program


MODULE 2: Creating a Java Main class
• Java classes
• The Main method
• Adding a Main method


MODULE 3: Data in the Cart
• Introducing variables
• Working with strings
• Working with numbers
• Manipulating numeric data


MODULE 4: Managing Multiple Items
• Working with conditions
• Using IF statements
• Working with a list of items
• Processing a list of items


MODULE 5: Describing Objects and Classes
• Working with objects and classes
• Defining fields and methods
• Declaring, instantiating, and initializing objects
• Working with object references
• Doing more with arrays

MODULE 6: Manipulating and Formatting the Data in Your Program
• Using the String class
• Using the Java API docs
• Using the StringBuilder class
• More about primitive data types
• More numeric operators
• Promoting and casting variables


MODULE 7: Creating and Using Methods
• Using methods
• Method arguments and return values
• Static methods and variables
• How arguments are passed to a method
• Overloading a method


MODULE 8: Using Encapsulation
• Access control
• Encapsulation
• Overloading constructors


MODULE 9: More on Conditionals
• Relational and conditional operators
• More ways to use IF/ELSE constructs
• Using switch statements
• Using the Netbeans debugger


MODULE 10: More on Arrays and Loops
• Working with dates
• Parsing the args array
• Two-dimensional arrays
• Alternate looping constructs
• Nesting loops
• The ArrayList class

MODULE 11: Using Inheritance
• Overview
• Working with subclasses and superclasses
• Overriding methods in the superclass
• Creating and extending abstract classes
MODULE 12: Using Interfaces
• Polymorphism
• Polymorphism in the JDK foundation classes
• Using interfaces
• Local-variable type inference
• Using the List interface
• Introducing Lambda expressions
• Handling Exceptions
• Overview
• Propagation of exceptions
• Catching and throwing exceptions
• Handling multiple exceptions and errors
MODULE 1: Linear Algebra
• Scalar, Vectors, Matrices & Tensor
• Lengths and Dot Products
• Solving Linear Equations
o Vector and Linear Equations
o Identity and Inverse Matrices
o Rules for Matrix Operations
o Transposes and Permutations
• Gaussian Elimination
• Norms
o L1/ L2 norm
MODULE 2: Probability and Statistics
• Mean, Variance and Standard Deviation
• Probability
o Conditional Probability
o Bayes Theorem
• Distributions
o Continuous distribution
  •  Normal / Gaussian distribution
o Discrete distribution
• Randomization
  • Random Variables
  • Random Number Generation + Seed Number
MODULE 3: Bayesian Inference
• Sets & Events
• Ways sets can interact
• Interaction of sets
• Union of sets
• Mutually exclusive sets
• Bay’s law

MODULE 1: Solving Business Problems Using AI and ML
• Topic A: Identify AI and ML Solutions for Business Problems
• Topic C: Formulate a Machine Learning Problem
• Topic D: Select Appropriate Tools


MODULE 2: Collecting and Refining the Dataset
• Topic A: Collect the Dataset
• Topic B: Analyze the Dataset to Gain Insights
• Topic C: Use Visualizations to Analyze Data
• Topic D: Prepare Data

MODULE 3: Setting Up and Training a Model
• Topic A: Set Up a Machine Learning Model
• Topic B: Train the Model
MODULE 4: Finalizing a Model
• Topic A: Translate Results into Business Actions
• Topic B: Incorporate a Model into a Long-Term Business Solution

MODULE 5: Building Linear Regression Models
• Topic A: Build a Regression Model Using Linear Algebra
• Topic B: Build a Regularized Regression Model Using Linear Algebra
• Topic C: Build an Iterative Linear Regression Model


MODULE 6: Building Classification Models
• Topic A: Train Binary Classification Models
• Topic B: Train Multi-Class Classification Models
• Topic C: Evaluate Classification Models
• Topic D: Tune Classification Models

MODULE 7: Building Clustering Models
• Topic A: Build k-Means Clustering Models
• Topic B: Build Hierarchical Clustering Models
MODULE 8: Building Advanced Models
• Topic A: Build Decision Tree Models
• Topic B: Build Random Forest Models
MODULE 9: Building Support-Vector Machines
• Topic A: Build SVM Models for Classification
• Topic B: Build SVM Models for Regression

MODULE 10: Building Artificial Neural Networks
• Topic A: Build Multi-Layer Perceptrons (MLP)
• Topic B: Build Convolutional Neural Networks (CNN)
MODULE 11: Promoting Data Privacy and Ethical Practices
• Topic A: Protect Data Privacy
• Topic B: Promote Ethical Practices
• Topic C: Establish Data Privacy and Ethics Policies

MODULE 1: Advanced Deep Learning with Computer Vision I: Basic Image Processing
• Display, load and save image
• Basic of the image
• Drawing
• Image Processing Fundamental I:
  • Rotation
  • Translation
  • Resizing
  • Flipping Cropping
  • Image Arithmetic
  • Bitwise Operation
  • Masking
  • Splitting and Merging Channels
• Image Filtering
  • Spatial filtering
  • Smoothing
  • Image noise
  • Sharpening
  • Filters
  • Convolution
  • Image Averaging (smoothing)
  • Gaussian Filter
  • Unsharp masking
• Kernels
• Morphological Operations
• Smoothing and Blurring

MODULE 2: Image Processing Fundamentals II:
• Edge Detection
o High pass filter
o Edge detection
o Sobel filter
o Canny edge detection
o Laplacian filter
o Laplacian of gaussian
• Histogram Processing
o Histogram processing
o Histogram equalization
• Color Image Processing
o Color fundamentals
o Color models
• Computer Vision with Machine Learning Techniques
o Image classification
 Image classification using SVM
o Object Detection
 Histogram of oriented gradients (HOG)
 Object detection using HOG and SVM classifier
o Face Detection
 Face detection using Haar Cascade Classifier
o Pre and Post Processing Techniques
 Image pyramid
 Non-max suppression (NMS)
• Image Processing Fundamental
• Simple Image Classification with Python

MODULE 3: Advanced Deep Learning with Computer Vision III: Object Detection
• Problem definition
o Object localization & Object classification
• Object detection
o Sliding window detection
o Convolutional implementation of sliding windows
• Bounding boxes predictions - YOLO
• Intersection over union
• Non-max suppression
• Hands-on: Avocado and banana object detection with TinyYOLO
MODULE 4: Advanced Deep Learning with Computer Vision IV: Image Segmentation
• Semantic vs Instance
• Semantic - architectures - FCN, UNet, SegNet, & a summary of other available architectures
• Different upsampling concepts
• Semantic Segmentation evaluation metrics
• Some popular datasets for training
• Applications
• Hands-on: Cell nucleus segmentation with UNet (data science bowl 2018)

MODULE 5: Advanced Deep Learning with Computer Vision V: Face Recognition and Modeling
• Face recognition and modeling
• Hands-on: Face recognition use case
MODULE 6: Advanced Deep Learning with Computer Vision VI: Image Generation, Style Transfer
• Generative adversarial network (GAN)
• Style Transfer
• Hands-on:
o Monalisa image generation
o Image style transfer

MODULE 7: Advanced Deep Learning with Computer Vision VI: Pose Estimation
• Pose estimation workflow breakdown
• Hands-on: Pose estimation use case
MODULE 8: Advanced Deep Learning with Computer Vision: Mini Project & Revision
• Hackathon-like activity
• Provide a theme, students are required to develop the solution and present it to the trainer

Deep Learning Engineer Examination


Tentatively in August & September 2020 ; maximum 19 pax per class

100% funded for Unemployed graduates, Unemployed Working Adult and Retrenched worker (T&C)

Yes, if you have skill and knowledge in Python/Java Programming, Artificial Intelligence & Deep Learning, but you need to pay for the training course RM10,000

  • To complete the Pre-Assessment from link provided
  • You will be received an email link for TET Assessment from to complete the TET test
  • Only successful candidate will be invited for the Online Briefing and Interview
  • Selected candidate will be enrolled for the program
    *Candidate will be notified through email – please check your Inbox/Junk mail

  • Yes, require 100% commitment for attendance ; absence with valid reason.

  • We allow you to sit for the 2nd attempt of exam with FOC ; (if require) MUST re-sit the exam

  • Not compulsory but we encourage you to sit for next attempt ; however, candidate need to pay for the Exam Fees RM1,500

  • Exam result will take about 1-2weeks.
  • Employment status within 1 month – *Skymind will contact the successful candidate directly

Who should attend?

Skills & Knowledge

Academic Qualification

How much the cost?


Course and Exam Price

Fully Funded