Algorithms For Beginners

(ALGO.AE1)/ISBN:978-1-64459-539-8

This course includes
Lessons
Lab
Algorithms For Beginners

The Algorithms For Beginners course helps you explore fundamental algorithmic concepts, such as time complexity, sorting techniques, and recursive problem-solving. With this course you will learn how to design, analyze, and implement efficient algorithms to tackle a wide range of real-world challenges. Additionally, you'll have the skills and confidence to apply algorithmic principles to your own projects, setting you up for success in any coding-centric career or further studies in computer science.

Lessons

23+ Lessons | 40+ Exercises | 150+ Quizzes | 116+ Flashcards | 116+ Glossary of terms

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Here's what you will learn

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Lessons 1: Introduction

  • About This Course
  • False Assumptions
  • Icons Used in This Course
  • Where to Go from Here

Lessons 2: Introducing Algorithms

  • Describing Algorithms
  • Using Computers to Solve Problems
  • Distinguishing between Issues and Solutions
  • Structuring Data to Obtain a Solution

Lessons 3: Considering Algorithm Design

  • Starting to Solve a Problem
  • Dividing and Conquering
  • Learning that Greed Can Be Good
  • Computing Costs and Following Heuristics
  • Evaluating Algorithms

Lessons 4: Working with Google Colab

  • Defining Google Colab
  • Working with Notebooks
  • Performing Common Tasks
  • Using Hardware Acceleration
  • Executing the Code
  • Getting Help

Lessons 5: Performing Essential Data Manipulations Using Python

  • Performing Calculations Using Vectors and Matrixes
  • Creating Combinations the Right Way
  • Getting the Desired Results Using Recursion
  • Performing Tasks More Quickly

Lessons 6: Developing a Matrix Computation Class

  • Avoiding the Use of NumPy
  • Understanding Why Using a Class is Important
  • Building the Basic Class
  • Manipulating the Matrix

Lessons 7: Structuring Data

  • Determining the Need for Structure
  • Stacking and Piling Data in Order
  • Working with Trees
  • Representing Relations in a Graph

Lessons 8: Arranging and Searching Data

  • Sorting Data Using Merge Sort and Quick Sort
  • Using Search Trees and the Heap
  • Relying on Hashing

Lessons 9: Understanding Graph Basics

  • Explaining the Importance of Networks
  • Defining How to Draw a Graph
  • Measuring Graph Functionality
  • Putting a Graph in Numeric Format

Lessons 10: Reconnecting the Dots

  • Traversing a Graph Efficiently
  • Sorting the Graph Elements
  • Reducing to a Minimum Spanning Tree
  • Finding the Shortest Route

Lessons 11: Discovering Graph Secrets

  • Envisioning Social Networks as Graphs
  • Navigating a Graph

Lessons 12: Getting the Right Web page

  • Finding the World in a Search Engine
  • Explaining the PageRank Algorithm
  • Implementing PageRank
  • Going Beyond the PageRank Paradigm

Lessons 13: Managing Big Data

  • Transforming Power into Data
  • Streaming Flows of Data
  • Sketching an Answer from Stream Data

Lessons 14: Parallelizing Operations

  • Managing Immense Amounts of Data
  • Working Out Algorithms for MapReduce

Lessons 15: Compressing and Concealing Data

  • Making Data Smaller
  • Hiding Your Secrets with Cryptography

Lessons 16: Working with Greedy Algorithms

  • Deciding When It Is Better to Be Greedy
  • Finding Out How Greedy Can Be Useful

Lessons 17: Relying on Dynamic Programming

  • Explaining Dynamic Programming
  • Discovering the Best Dynamic Recipes

Lessons 18: Using Randomized Algorithms

  • Defining How Randomization Works
  • Putting Randomness into your Logic

Lessons 19: Performing Local Search

  • Understanding Local Search
  • Presenting Local Search Tricks
  • Solving Satisfiability of Boolean Circuits

Lessons 20: Employing Linear Programming

  • Using Linear Functions as a Tool
  • Using Linear Programming in Practice

Lessons 21: Considering Heuristics

  • Differentiating Heuristics
  • Routing Robots Using Heuristics
  • Explaining Path Finding Algorithms

Lessons 22: Ten Algorithms That Are Changing the World

  • Using Sort Routines
  • Looking for Things with Search Routines
  • Shaking Things Up with Random Numbers
  • Performing Data Compression
  • Keeping Data Secret
  • Changing the Data Domain
  • Analyzing Links
  • Spotting Data Patterns
  • Dealing with Automation and Automatic Responses
  • Creating Unique Identifiers

Lessons 23: Ten Algorithmic Problems Yet to Solve

  • Solving Problems Quickly
  • Solving 3SUM Problems More Efficiently
  • Making Matrix Multiplication Faster
  • Determining Whether an Application Will End
  • Creating and Using One-Way Functions
  • Multiplying Really Large Numbers
  • Dividing a Resource Equally
  • Reducing Edit Distance Calculation Time
  • Playing the Parity Game
  • Understanding Spatial Issues

Hands-on LAB Activities (Performance Labs)

Performing Essential Data Manipulations Using Python

  • Performing Logical Operations
  • Using Comparison Operators
  • Performing Data Manipulations
  • Finding the Factorial of a Number Using Recursion

Developing a Matrix Computation Class

  • Using Matrix Operations
  • Flattening a Matrix

Structuring Data

  • Dealing with Missing Values
  • Removing the Duplicate Records
  • Creating and Traversing a Binary Tree

Arranging and Searching Data

  • Implementing Quick Sort
  • Implementing Merge Sort
  • Building and Searching a Binary Heap

Understanding Graph Basics

  • Implementing a Graph
  • Adding a Graph to a Matrix
  • Using Sparse Representations

Reconnecting the Dots

  • Creating a Minimum Spanning Tree
  • Adding a Negative Edge to a Graph
  • Using the Floyd-Warshall Algorithm

Getting the Right Web page

  • Creating a Network with a Spider Trap

Managing Big Data

  • Demonstrating the Bloom Filter

Parallelizing Operations

  • Using the lambda Function
  • Setting up a MapReduce Simulation

Compressing and Concealing Data

  • Using Compression
  • Using Encryption

Working with Greedy Algorithms

  • Using the change Function

Relying on Dynamic Programming

  • Printing a Fibonacci Sequence Using Recursion
  • Using Dynamic Programming

Using Randomized Algorithms

  • Creating a Histogram
  • Creating a Monte Carlo Simulation
  • Implementing the Quick Select Algorithm
  • Computing the Median of a Series

Employing Linear Programming

  • Visualizing Data Using Python Plotting Functions

Considering Heuristics

  • Creating a Maze