Why Python for Kids for Dummies?
EXCLUSIVE
You will be one of the first in the world to hold the exclusive dummies endorsed certificate.
FUN & ENRICHING
The course approaches Python coding in a fun, easy-to-understand way.
PROJECT-BASED
You'll be creating apps and games with Python before you even realise it.
HIGH VALUE
In only one course, add Python coding and an internationally recognised certificate to your portfolio.
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FUN FACTS
Curriculum
Level 1
Learn the basics of Python and what you can do with it, then dive straight into creating projects with Python 2.7.
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Topics Covered:
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Programme Introduction
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Python Introduction
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Project: Hello World!
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Project: Creating a Guessing Game
Level 2
Learn more about the functions and settings needed to write good code that can be transferred to other projects.
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Topics Covered:
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Set Up Your Coding Environment
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A Better Guessing Game
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Schedule
Level 1 Only
Level 1 & 2
CLASS 2: 14 & 15 DEC
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Mon & Tue, 10am - 1.30pm
@ Online
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CLASS 3: 16 & 17 DEC
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Wed & Thu, 10am - 1.30pm
@ Online
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CLASS 4: 18 DEC
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Fri, 10am - 5pm
(Lunch break: 1pm - 2pm)
@ Online
CLASS 3: 12 & 13 DEC (FULL)
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Sat & Sun, 10am - 5pm
(Lunch break: 1pm - 2pm)
@ Online
What You Will Get:
Features
Level 1
Level 2
Level 1 & 2
Free Python for Kids eBook
Live instruction by expert instructors
Dummies endorsed certificate
Prerequisites
Level 1
Price
$275
$275
$549
Learning Made Easy
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INTRODUCTIONIntroduction What is AI good for?
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GRAPH-SEARCH ALGORITHMSBreadth-first search introduction Breadt-first search implementation Depth-first search introduction Depth-first search implementation I - with stack Depth-first search implementation II - with recursion Enhanced search algorithms introduction Iterative deepening depth-first search (IDDFS) A* search introduction
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BASIC SEARCH & OPTIMIZATION ALGORITHMSBrute-force search introduction Brute-force search example Stochastic search introduction Stochastic search example Hill climbing introduction Hill climbing example
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META-HEURISTIC OPTIMIZATION METHODSHeuristics VS meta-heuristics Tabu search introduction Simulated annealing introduction Simulated annealing - function extremum I Simulated annealing - function extremum II Simulated annealing - function extremum III Travelling salesman problem I - city Travelling salesman problem II - tour Travelling salesman problem III - annealing algorithm Travelling salesman problem IV - testing Genetic algorithms introduction - basics Genetic algorithms introduction - chromosomes Genetic algorithms introduction - crossover Genetic algorithms introduction - mutation Genetic algorithms introduction - the algorithm Genetic algorithm implementation I - individual Genetic algorithm implementation II - population Genetic algorithm implementation III - the algorithm Genetic algorithm implementation IV - testing Genetic algorithm implementation V - function optimum Swarm intelligence intoduction Partical swarm optimization introduction I - basics Partical swarm optimization introduction II - the algorithm Particle swarm optimization implementation I - particle Particle swarm optimization implementation II - initialize Particle swarm optimization implementation III - the algorithm Particle swarm optimization implementation IV - testing
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MINIMAX ALGORITHM - GAME ENGINESGame trees introduction Minimax algorithm introduction - basics Minimax algorithm introduction - the algorithm Minimax algorithm introduction - relation with tic-tac-toe Alpha-beta pruning introduction Alpha-beta pruning example Chess problem
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BUILDING TIC-TAC-TOEAbout the game Cell Constants and Player Game implementation I Game implementation II Board implementation I Board implementationj II - isWinning() Board implementation III Minimax algorithm Running tic-tac-toe
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INTERVIEW: SINGAPOREAN EXPERTBackground of Expert Information and Communication Technology in Singapore