Algorithms For Beginners
Discover the fundamentals of Data Structure and Algorithms for upgrading your problem-solving skills.
(ALGO.AE1) / आईएसबीएन : 978-1-64459-539-8इस पाठ्यक्रम में शामिल हैं
Lessons
Lab
इस कोर्स के बारे में
This is a basic Algorithms for beginner’s course that teaches you the fundamentals to design, analyze, and implement effective algorithm techniques for solving real-world problems. Explore the core algorithmic concepts including sorting techniques, mapping time-complexities and resolving issues in a systematic manner. Learn about different types of algorithms, and how to analyze and implement them using Python. Additionally, this beginner’s guide to Algorithms is equipped with interactive lessons, hands-on exercises, and practical assignments that enable the learners to practice their knowledge for enhanced learning and better skill development.
कौशल जो आपको प्राप्त होंगे
- Create algorithms to solve computational problems
- Design, analyze, and implement efficient algorithms
- Utilize data structures like arrays, linked lists, stacks, queues, trees, graphs
- Ability to present and manipulate data effectively
- Skilled in Python programming
- Understanding of Python syntax, control flow, functions, and object-oriented programming concepts
- Expertise in identifying and resolving problems systematically
आपको जिस सहायता की आवश्यकता है उसे प्राप्त करें। हमारे प्रशिक्षक-नेतृत्व वाले पाठ्यक्रम में नामांकन करें।
पाठ
23+ पाठ | 40+ अभ्यास | 150+ प्रश्नोत्तरी | 116+ फ़्लैशकार्ड | 116+ पारिभाषिक शब्दावली
1
Introduction
- About This Course
- False Assumptions
- Icons Used in This Course
- Where to Go from Here
2
Introducing Algorithms
- Describing Algorithms
- Using Computers to Solve Problems
- Distinguishing between Issues and Solutions
- Structuring Data to Obtain a Solution
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
4
Working with Google Colab
- Defining Google Colab
- Working with Notebooks
- Performing Common Tasks
- Using Hardware Acceleration
- Executing the Code
- Getting Help
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
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
7
Structuring Data
- Determining the Need for Structure
- Stacking and Piling Data in Order
- Working with Trees
- Representing Relations in a Graph
8
Arranging and Searching Data
- Sorting Data Using Merge Sort and Quick Sort
- Using Search Trees and the Heap
- Relying on Hashing
9
Understanding Graph Basics
- Explaining the Importance of Networks
- Defining How to Draw a Graph
- Measuring Graph Functionality
- Putting a Graph in Numeric Format
10
Reconnecting the Dots
- Traversing a Graph Efficiently
- Sorting the Graph Elements
- Reducing to a Minimum Spanning Tree
- Finding the Shortest Route
11
Discovering Graph Secrets
- Envisioning Social Networks as Graphs
- Navigating a Graph
12
Getting the Right Web page
- Finding the World in a Search Engine
- Explaining the PageRank Algorithm
- Implementing PageRank
- Going Beyond the PageRank Paradigm
13
Managing Big Data
- Transforming Power into Data
- Streaming Flows of Data
- Sketching an Answer from Stream Data
14
Parallelizing Operations
- Managing Immense Amounts of Data
- Working Out Algorithms for MapReduce
15
Compressing and Concealing Data
- Making Data Smaller
- Hiding Your Secrets with Cryptography
16
Working with Greedy Algorithms
- Deciding When It Is Better to Be Greedy
- Finding Out How Greedy Can Be Useful
17
Relying on Dynamic Programming
- Explaining Dynamic Programming
- Discovering the Best Dynamic Recipes
18
Using Randomized Algorithms
- Defining How Randomization Works
- Putting Randomness into your Logic
19
Performing Local Search
- Understanding Local Search
- Presenting Local Search Tricks
- Solving Satisfiability of Boolean Circuits
20
Employing Linear Programming
- Using Linear Functions as a Tool
- Using Linear Programming in Practice
21
Considering Heuristics
- Differentiating Heuristics
- Routing Robots Using Heuristics
- Explaining Path Finding Algorithms
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
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
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
