Overview

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Learn Python from one of the most interactive and thorough online web materials available.
With the Programming in Python zyBook, you will learn the essential concepts of Python programming, practice your new skills, and put them to the test with challenge questions – all in one zyBook package.
Research Proven Excellence
Preferred Choice
80% of surveyed students preferred zyBooks over regular textbooks.
Better Learning
Studies show that students learn 118% more with the minimal text model in only one lesson.
Proven Results
On average, zyBook users improved grades up to ⅔ and read 74% more than users of regular textbooks.
What is a zyBook?
In short, zyBooks are interactive, digital textbooks.
By incorporating available technology, zyBooks make learning fun, interactive and engaging – all while drastically reducing the time spent on learning theoretical concepts.
What is a zyLab?
zyLabs are online lab environments that are integrated into their respective zyBooks.
Through zyLabs, you can practice your new skills and obtain immediate feedback and grades. No additional downloading of software required.
Course Details
The Programming in Python zyBook introduces essential Python Programming concepts and practices with minimal text, maximum interactivity.
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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
What Our Students Say
Our Other zyBooks & Courses
The Programming in Python zyBook will pair well with the following:
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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