BECOME A CERTIFIED DEEP LEARNING ENGINEER!
Get set for a career as a Deep Learning Engineer with salary starting from RM4,000


CERTIFIED IN DEEP LEARNING ENGINEER
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:
- Python and Java Programming with Essential Maths.
- Artificial Intelligence (AI) Practitioner.
- Deep Learning with SkyMind.
Candidate will be offered with guaranteed job placement with starting salary RM4,000 upon passing the exam.
BOOTCAMP SCHEDULE OUTLINES
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
- Normal / Gaussian distribution
- Random Variables
- Random Number Generation + Seed Number
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 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
- Rotation
- Translation
- Resizing
- Flipping Cropping
- Image Arithmetic
- Bitwise Operation
- Masking
- Splitting and Merging Channels
- Spatial filtering
- Smoothing
- Image noise
- Sharpening
- Filters
- Convolution
- Image Averaging (smoothing)
- Gaussian Filter
- Unsharp masking

FAQ
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 https://tinyurl.com/yaunexel
- You will be received an email link for TET Assessment from pmo@knowledgecom.my 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?
- Unemployed Fresh Graduates
- Retrenched Workers
- Working Adults
Skills & Knowledge
- Python/Java Programming,
- Artificial Intelligence,
- Deep Learning Framework & Technique
Academic Qualification
- Successfully acquired Degree in Actuarial Science, Technology, Engineering & Computer Science with CGPA > 3.0
How much the cost?
- Unemployed Fresh Graduate - Fully funded
- Retrenched Workers - Fully funded
- Working adults - RM10,000 (self paying)
BECOME A CERTIFIED DEEP LEARNING ENGINEER!
