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STAY RELEVANT IN THE DIGITAL AGE

Overview

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Want to build a strong theoretical foundation but sick of long readings?

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Gone are the days where you have to spend hours reading thick textbooks and readings. With zyBooks, you can learn more about your selected topic with minimal reading, maximum interactivity.

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.

Topics
Data Analytics

DATA ANALYTICS

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Applied Statistics for Data Analytics with Python

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Statistics of Data Analytics

Computer Science

What Our Students Say

“I really enjoyed zyBooks for use in my Python class. It has surely aided my success in class and helped me build some confidence in my first year at university.”
 

Isaac C.

Cal State University, Long Beach

Testimonials
FAQ
  • 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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