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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is an Algorithm?

Ayushi Trivedi
Ayushi Trivedi Last Updated : 03 Jun, 2024
6 min read

Introduction

This article will provide you with a thorough understanding of algorithms, which are necessary steps in problem solving and processing. We’ll explore the principles of algorithms, the different kinds of them, and the wide range of uses they have in disciplines like machine learning, data science, and daily life. Algorithms are integral to automating and improving operations, ranging from sorting numbers to facilitating facial recognition and controlling traffic signals. This guide will bring you important insights into the realm of algorithms, regardless of your level of experience.

what is an algorithm?

Table of contents

What is an Algorithm?

An algorithm is a step-by-step procedure used for solving a problem or performing a computation. It acts as an exact list of instructions that conduct specified actions in a precise sequence. They can be implemented in both hardware- and software-based routines and are widely used throughout all areas of IT. In mathematics, computer programming, and computer science, an algorithm usually refers to a small procedure that solves a recurrent problem. They are also essential in data processing and automated systems.

Key Characteristics of Algorithms

  • Finiteness: It must always terminate after a finite number of steps.
  • Definiteness: Steps must all be clearly defined, and each case’s necessary actions must be stated in detail.
  • Input: Inputs, or the data elements it works with, can be zero or more.
  • Output: One or more outputs, or the outcomes of the calculation or processing, are generated by an algorithm.
  • Effectiveness: The operations to be performed must be sufficiently basic that they can be done exactly and in a finite length of time.

Types of Algorithms

Let us explore types of algorithms that are possible:

  • Search Algorithms: These algos are employed for data structure element searches. Examples include:
    • Linear Search
    • Binary Search
  • Sorting Algorithms: The items are arranged in a specific order using these algos. Examples include:
    • Bubble Sort
    • Merge Sort
    • Quick Sort
  • Divide and Conquer Algorithms: In order to solve the main problem, these algos break the problem down into smaller subproblems, solve each subproblem separately, and then combine their results. Examples include:
    • Merge Sort
    • Quick Sort
  • Dynamic Programming Algorithms: These algos solve problems by breaking them down into simpler sub-problems and storing the results of these sub-problems to avoid redundant computations. Examples include:
    • Fibonacci Sequence
    • Knapsack Problem
  • Greedy Algorithms: In an attempt to locate a global optimum, these algos select the option that is locally optimal at each step. Examples include:
    • Kruskal’s Algorithm
    • Prim’s Algorithm
  • Backtracking Algorithms: These algos try to build a solution incrementally, removing those solutions that fail to satisfy the constraints of the problem at any point of time. Examples include:
    • N-Queens Problem
    • Sudoku Solver

Steps to Design Algorithm

Let us now look into steps involved into designing an algorithm.

1. Problem Definition

The initial step in algo design is to clearly define the problem and understand its requirements. This involves identifying the problem, constraints, and the expected output. For instance, to find the maximum number in a list of integers, one must understand the inputs and expected output. This ensures a clear understanding of the algo’s objectives.

2. Input/Output Specification

The problem is defined, followed by specifying the algo’s inputs and outputs. Inputs are data elements or parameters, like an unsorted list of integers in a sorting algorithm. The format and constraints of each input are crucial. Outputs are the results the algo produces based on the inputs, such as sorted integers in ascending or descending order. Defining these inputs and outputs ensures understanding the algo’s working data and outputs.

3. Algorithm Development

Clearly defining the problem and identifying the inputs and outputs are essential steps in creating an algorithm. This entails writing a detailed strategy or procedure, frequently in the form of pseudocode. For instance, you can initialize a variable with the first element in a list of integers and loop through it, updating it as larger numbers are found, to get the maximum number in the list.

4. Verification

The algorithm’s functionality is verified by testing it with various inputs, including normal, edge, and extreme cases. This ensures that the algorithm produces expected outputs. For instance, a sorting algorithm should check if it correctly sorts both small and large lists of integers, including those already sorted or reversed. Verification ensures the algorithm solves the problem as expected under different conditions.

5. Analysis

After verifying the algorithm’s functionality, analyze its efficiency in terms of time and space complexity. Time complexity, often represented by Big O notation, measures the algorithm’s growth with input data size. Space complexity measures the memory or space required for completion. Compare the algorithm’s performance with other solutions and consider optimization techniques if needed to improve efficiency.

Example Walkthrough

Problem Definition: You need to find the maximum number in a list of integers.

Input/Output Specification:

  • Input: A list of integers.
  • Output: The maximum integer in the list.

Algorithm Development:

  • Steps:
    • Initialize a variable max with the first element of the list.
    • Iterate through the list:
      • Update max if the current element is greater than max.
    • Return max.

Verification:

  • Test the algorithm with various lists:
    • [1, 2, 3, 4, 5] → Expected Output: 5
    • [5, 4, 3, 2, 1] → Expected Output: 5
    • [] → Expected Output: None (Empty list case)

Analysis:

  • Time Complexity: O(n) where n is the number of elements in the list.
  • Space Complexity: O(1) constant space, since only a few variables are used regardless of the input size.

Pseudocode Example

Here is an example of an algorithm written in pseudocode to find the maximum number in a list of numbers:

Algorithm FindMax
    Input: A list of numbers L
    Output: The maximum number in the list

    Step 1: Set Max to L[0]
    Step 2: For each number num in L
        If num > Max
            Set Max to num
    Step 3: Return Max

Applications of Algorithms

Let us now explore applications of algorithms.

Data Processing and Analysis

Algorithms are essential to data processing and analysis because they enable effective sorting, binary search, data compression, and search. They let businesses to use their data to make educated decisions by being crucial to databases, data mining, and business intelligence initiatives.

Computational Problem Solving

Complex computing issues including network architecture, logistics, and optimization challenges require the use of algorithms. In many different areas, including engineering, manufacturing, logistics, and transportation, they save costs and increase productivity.

Artificial Intelligence and Machine Learning

Algorithms are crucial in artificial intelligence and machine learning for model training, prediction, and decision-making. They discover structures, learn patterns, stimulate creativity, and enhance user experiences through reinforcement learning algorithms.

Optimization and Resource Allocation

In many different sectors, algorithms are essential for resource optimization and effective allocation. Allocation algorithms optimize resource distribution in logistics and supply chain management, scheduling algorithms oversee tasks in operating systems, and load balancing algorithms guarantee equitable workload distribution in cloud computing settings.

Networking and Routing

Algorithms are crucial for efficient data flow and routing in computer networks. Routing algorithms like OSPF and BGP determine data paths, while network flow algorithms like Ford-Fulkerson optimize resource utilization. Congestion control algorithms like TCP prevent congestion and ensure reliable communication.

Graphics and Image Processing

Algorithms are crucial for processing graphics, images, and multimedia data in various applications. Image filtering enhances quality, rendering generates realistic visual effects, and compression reduces data size. These algos are essential for immersive virtual environments, digital entertainment, medical imaging, and scientific visualization.

Natural Language Processing (NLP)

Algos play a major role in supporting cross-language communication, information retrieval, and content analysis in natural language processing applications like text processing, sentiment analysis, and machine translation. Virtual assistants and chatbots are examples of these applications.

What Makes a Good Algorithm?

In the fields of data science and computers, creating a strong algorithm is essential. A well-designed algo can greatly increase a system’s efficacy and efficiency. Whether an algo is efficient and appropriate for usage is determined by multiple principles:

  • Correctness: The algos should always produce the right output for any given input, free of errors and bugs.
  • Efficiency: efficient use of computer power, completing jobs quickly to save effort and time..
  • Simplicity: The algos should avoid needless complexity by being simple to comprehend, apply, and maintain.
  • Robustness: Capacity to smoothly manage faults and unexpected inputs, guaranteeing consistent and dependable performance.
  • Flexibility: ability to adjust to shifting circumstances and conditions, accepting many inputs and revisions without sacrificing performance.
  • Security: created with security in mind, guaranteeing the defense against harmful attacks and the preservation of critical data.

Conclusion

Algorithms are essential tools for automating tasks, optimizing hardware and software, and solving problems. They are crucial in many domains, including artificial intelligence, software development, data analysis, and computational problem solving. We may push the limits of computing and beyond by comprehending and using algos efficiently, fostering innovation, efficiency, and effectiveness in both daily life and technology.

Don’t miss this chance to develop your abilities and further your career. Come learn Python with us! We offer an extensive and captivating free course that is suitable for all levels.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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