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COURSE INFORMATION

PREREQUISITES
CURRICULUM
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COURSE

Spark for Python

DESCRIPTION

With Spark, you can have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

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PREREQUISITES

  • Knowledge of Python

  • Basic knowledge of Java

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CURRICULUM

  • 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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COURSE FEES

$309.98

You may be eligible for up to 100% of subsidy for your course fees.

Check here.

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INSTRUCTIONS FOR ENROLMENT

1. Check if you are eligible for subsidies here and follow the instructions.

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2. Purchase the e-module below:

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3. Purchase the face-to-face lesson below:

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4. Once you have completed 90% of your e-module, the Gen Infiniti team will contact you to schedule your in-person lesson.

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Drop us an email at connect@geninfinitiacademy.com if you face any issues or problems enrolling in the course.

COURSE FEES
ENROLL
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