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Programming Console

START YOUR DATA SCIENCE CAREER WITH PYTHON

Overview

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Kickstart your coding journey with the Wiley Certified Python Programmer course!

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Through the course, you will be able to perform a wealth of practical tasks using Python and become thoroughly conversant with the coding environment of Python, the library of functionalities and the multiple coding styles. You will also have the opportunity to perform multiple labs to make you confident of your Python coding skills.

200 Years of Excellence

WILEY is one of the top four publishers and research institutions worldwide.

Strong Industry Partnerships

As co-chair of the Project DARE Advisory Group, WILEY works closely with governments, businesses and academia.

For the Industry, By the Industry

Curriculum created after intensive research and interviews with stakeholders in the data analytics industry.

Course Details

The course will be a mixture of e-learning content (Dedicated Online Learning Management System) and in-person training by our WILEY Certified Instructors.

 

In total, there will be six classes of 3 hours each.

  • INTRODUCTION
    Introduction What is AI good for?
  • GRAPH-SEARCH ALGORITHMS
    Breadth-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
  • BASIC SEARCH & OPTIMIZATION ALGORITHMS
    Brute-force search introduction Brute-force search example Stochastic search introduction Stochastic search example Hill climbing introduction Hill climbing example
  • META-HEURISTIC OPTIMIZATION METHODS
    Heuristics 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
  • MINIMAX ALGORITHM - GAME ENGINES
    Game 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
  • BUILDING TIC-TAC-TOE
    About 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
  • INTERVIEW: SINGAPOREAN EXPERT
    Background of Expert Information and Communication Technology in Singapore

Class Schedules

March 2021 Classes

Class 1: 6 March - 20 March

Sat, 10am - 5pm

@ Zoom Online

What Our Students Say

  • INTRODUCTION
    Introduction What is AI good for?
  • GRAPH-SEARCH ALGORITHMS
    Breadth-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
  • BASIC SEARCH & OPTIMIZATION ALGORITHMS
    Brute-force search introduction Brute-force search example Stochastic search introduction Stochastic search example Hill climbing introduction Hill climbing example
  • META-HEURISTIC OPTIMIZATION METHODS
    Heuristics 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
  • MINIMAX ALGORITHM - GAME ENGINES
    Game 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
  • BUILDING TIC-TAC-TOE
    About 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
  • INTERVIEW: SINGAPOREAN EXPERT
    Background of Expert Information and Communication Technology in Singapore
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