Algorithms: 3 books in 1: Practical Guide to Learn Algorithms For Beginners + Design Algorithms to Solve Common Problems + Advanced Data Structures for Algorithms

Length: 554 pages
Edition: 1
Language: English
Publication Date: 2021-12-20
ISBN-10: B09NYRMYP7
Sales Rank: #0 (See Top 100 Books)

Book 1

Have you ever wondered how a programmer develops games and writes code without having to think too much? Do you want to know what makes a programmer confident about the code they write? Do you want to learn how programmers use algorithms to determine how to structure their programs before they develop it? If you did, this is the book for you.

An algorithm is a set of rules or instructions you provide to a system. The system performs a specific process to answer a question using these instructions. As an amateur or expert, it is important for you to understand what an algorithm is and how you should define it. Once you learn to develop an algorithm, you can easily learn to develop code to execute that algorithm. Throughout this book, you will learn about the following:
• Algorithms and their characteristics
• How you should define the algorithm
• Types of algorithms
• Analyzing an algorithm based on time and space complexities
• Writing code keeping an algorithm in mind
• And more!

This book will also show you how to implement the sorting and searching algorithms using C and Java programming languages, since these are the most common languages used by programmers. Bear in mind an algorithm is only a set of instructions, and this means you can use an algorithm to write code using different programming languages. You will only need to understand the properties of the programming language and use the right syntax.

Book 2

Are you interested in furthering your knowledge of algorithms ? Do you want to learn how they work for real-world problems? Then you’ve come to the right place. This guide will walk you through algorithm design before digging into some of the top design techniques.

Here’s what you will learn:
• The steps involved in designing an algorithm
• The top algorithm design techniques
• The Divide and Conquer algorithm
• The Greedy Algorithm
• Dynamic Programming
• The Branch and Bound Algorithm
• The Randomized Algorithm
• Recursion and backtracking
And everything that goes with them.

Included are plenty of algorithm designs and code implementations to show you how it all works.

Book 3

Are you studying data science and want to take your learning further ? Data structures are an integral part of data science, machine learning, and algorithms, all aimed at solving programming challenges that might seem insurmountable at the outset. Advanced Data Structures for Algorithms builds on your current knowledge, taking your learning much deeper and teaching you how to solve even the trickiest of challenges.

This book has been divided into four parts:
Part One covers advanced lists, including:
• An overview of linked lists
• Doubly linked lists
• XOR linked lists
• Self-organizing lists
• Unrolled linked lists
Part Two covers trees, including:
• Segment trees
• Trie trees
• Fenwick trees
• AVL trees
• Red-black trees
• Scapegoat trees
• Treap
• N-ary
Part Three discusses disjoint sets or Union-finds, as they are sometimes known
Part Four covers heaps and priority queues, including:
• A brief discussion on binary heaps
• Binomial heaps
• Fibonacci heaps
• Leftist heaps
• K-ary heaps
• Iterative heapsorts

You’ll find plenty of code examples to help you make sense of things and common-sense explanations.

If you want to advance your knowledge of data structures for algorithms you are in the right place.

ALGORITHMSPractical Guide to Learn Algorithms for Beginners
ALGORITHMSPractical Guide to Learn Algorithms for Beginners
Introduction
Chapter One: Introduction to Algorithms
Chapter One: Introduction to Algorithms
    Association between Algorithms and Computer Science
    Characteristics of an Algorithm
    Designing an Algorithm
    How to Identify the Best Algorithm
    Understanding the Basic Algorithm that Digitally Powers Life
    Benefits of Algorithmic Thinking
    Pros and Cons
Chapter Two: Types of Algorithms
Chapter Two: Types of Algorithms
    Backtracking Algorithm
    Brute Force Algorithm
    Divide and Conquer Algorithm
    Dynamic Programming Algorithm
    Greedy Algorithm
    Randomized Algorithm
    Simple Recursive Algorithm
Chapter Three: Describing Algorithms
Chapter Three: Describing Algorithms
Chapter Four: Error Handling
Chapter Four: Error Handling
    Checking for Exceptions
    Defining Exceptions
    Special Case Patterns
    Nulls
    Common Error Messages
    Dealing with errors
Chapter Five: Analysis of Algorithms
Chapter Five: Analysis of Algorithms
    Importance of Analysis
    Analysis Methods
    Space Complexities
    Understanding Recursion
Chapter Six: An Introduction to Writing Programs
Chapter Six: An Introduction to Writing Programs
    Principles of Programming
    Objects and Classes
    Data Types
    Operations
Chapter Seven: Types of Programming Languages
Chapter Seven: Types of Programming Languages
    Definition
    Types of Programming Languages
    Programming Languages
Chapter Eight: Important Programming Techniques
Chapter Eight: Important Programming Techniques
    Arrays
    Building Big Programs
    Bitwise Logic
    Boolean Logic
    Closures
    Concurrency
    Decision or Selection
    Disk Access
    Immutability
    Interacting with the Command Line
    Interacting with the OS
    Lambdas
    Loops and Repetitions
    Linked Lists
    Modular Arithmetic
    Pointers
    Safe Calls
    Scaling and Random Numbers
    Strings
    Structures
    Text Manipulation
    Trigonometry
    Variables
Chapter Nine: Testing the Program
Chapter Nine: Testing the Program
    Laws of TTD
    Keeping the Tests Clean
    Testing the Abilities of the Code
    Clean Tests
    Characteristics of Tests
Chapter Ten: Sorting and Searching Algorithms
Chapter Ten: Sorting and Searching Algorithms
    Searching Algorithms
    Sorting Algorithms
Chapter Eleven: Loop Control and Decision Making
Chapter Eleven: Loop Control and Decision Making
    Decision Making
    Loop Statements
    Loop Control Statements
Chapter Twelve: Introduction to Data Structures
Chapter Twelve: Introduction to Data Structures
    The Struct Statement
    Accessing Structure Members
    Using Structures as Arguments
    Using Pointers in Structures
    Typedef Keyword
Chapter Thirteen: Comments and Formatting
Chapter Thirteen: Comments and Formatting
    Comments
    Formatting
Chapter Fourteen: Debugging
Chapter Fourteen: Debugging
Conclusion
Resources
ALGORITHMSDesign Algorithms to Solve Common Problems
ALGORITHMSDesign Algorithms to Solve Common Problems
Introduction
Chapter 1: Designing an Algorithm
Chapter 1: Designing an Algorithm
    Designing an Algorithm
    Algorithm Design Techniques
Chapter 2: Divide and Conquer
Chapter 2: Divide and Conquer
    Quicksort
    Mergesort
    Closest Pair of Points
Chapter 3: Greedy Algorithms
Chapter 3: Greedy Algorithms
    Creating a Greedy Algorithm
    Greedy Algorithms Examples
    Graph Coloring Greedy Algorithm
    Building a Huffman Tree
Chapter 4: Dynamic Programming
Chapter 4: Dynamic Programming
    Subproblems
    Memoization with Fibonacci Numbers
    Dynamic Programming Process
    Paradox of Choice: Multiple Options Dynamic Programming
    Runtime Analysis of Dynamic Programs
    Dynamic Programming Algorithms
    Bellman-Ford Algorithm
Chapter 5: Branch and Bound
Chapter 5: Branch and Bound
    Knapsack Using Branch and Bound
    Branch and Bound
    Using Branch and Bound to Generate Binary Strings of Length N
Chapter 6: Randomized Algorithm
Chapter 6: Randomized Algorithm
    Conditional Probability
    Random Variables
    How to Analyze Randomized Algorithms
    Randomized Algorithms – Classification and Application
Chapter 7: Recursion and Backtracking
Chapter 7: Recursion and Backtracking
    Recursion
    Backtracking
    The Knight's Tour Problem
    Subset Sum
Conclusion
Resources
AlgorithmsAdvanced Data Structures for Algorithms
AlgorithmsAdvanced Data Structures for Algorithms
Introduction
Part 1: Advanced Lists
Part 1: Advanced Lists
    Linked Lists
    Doubly Linked List
    XOR Linked Lists
    Self-Organizing Lists
    Unrolled Linked List
Part 2: Advanced Trees
Part 2: Advanced Trees
    Segment Trees
    Trie Data Structures
    Fenwick Tree
    AVL Tree
    Red Black Tree
    Scapegoat Trees
    Treap
    N-ary Tree
Part 3: Disjoint Sets
Part 3: Disjoint Sets
    Disjoint Set Data Structures
Part 4: Advanced Heaps and Priority Queues
Part 4: Advanced Heaps and Priority Queues
    Binary Heap or Binary Search Tree for Priority Queues?
    Binomial Heap
    Fibonacci Heap
    Leftist Heap
    K-ary Heap
    Heapsort/Iterative HeapSort
Conclusion
References