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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

A Quick Guide to Blockchain: Merkle Tree

ANURAG SINGH CHOUDHARY
ANURAG SINGH CHOUDHARY Last Updated : 11 May, 2023
5 min read

Introduction

A Merkle tree is a basic component of blockchain technology. It is a mathematical data structure composed of hashes of different data blocks that serve as a summary of all transactions in the block. It also enables efficient and secure verification of the content of large amounts of data. Both Bitcoin and Ethereum use the Merkle Trees structure. Merkle Tree is also described as a Hash Tree.

The Merkle Tree concept is named after Ralph Merkle, who patented the idea in 1979. It is a tree data structure in which each leaf node is labeled with a data block hash, and a non-leaf node is labeled with the cryptographic hash labels of its child nodes. Leaf nodes are the last ending node in the tree.

Merkle Tree in Blockchain

Source :dev.to/

This article was published as a part of the Data Science Blogathon.

Table of contents

Working of Merkle Tree

A Merkle tree stores all transactions in a block by creating a digital fingerprint of the entire set of transactions. It permits the user to confirm whether a transaction can be covered in a block or not.

Computing hash pairs of nodes develop Merkle trees until only one hash remains. This hash is described as Root Hash or Merkle Root. Merkle Trees are constructed using a bottom-up approach.

Working of Merkle Tree

Source: researchgate.net

Each leaf node is a hash of transaction data, and a non-leaf node is a hash of its previous hashes. Merkle trees are part of a binary tree; therefore, it necessitates an even number of leaf nodes. If Transactions are in odd numbers, then the last hash will be repeated once to generate an even number of leaf nodes.

BInary Merkle Tree

Source: developer.bitcoin.org

The above example is the most common and simplest form of a Merkle tree, i.e., a binary Merkle tree. The number of transactions in the block is respectively TX1, TX2, TX3, and TX4. Here you can see that there is a top hash, the hash of the entire tree, known as the Root Hash or Merkle Root. Each of these is repeatedly hashed and stored in each leaf node, resulting in hashes 0, 1, 2, and 3. Consecutive pairs of leaf nodes are then summarized in the parent node by hashing Hash0 and Hash1, resulting in Hash01, and separately hashing Hash2 and Hash3; the result is Hash23. The two hashes (Hash01 and Hash23) are then hashed again to create the Root Hash or Merkle Root.

The Merkle Root is deposited in the block header. A block header is the part of a Bitcoin block that is hashed in the mining process. It contains the hash of the last block, the Nonce, and the Root Hash of all transactions in the current block in the Merkle Tree. The Merkle root directory in the block header makes the transaction tamper-proof. Because this root hash contains the hashes of all transactions within the block, these transactions can save disk space.

Merkle Tree

Source: selfkey.org

Merkle Tree maintains data integrity. If any individual detail of transactions or the order of transactions changes, those changes will be reflected in the hash of that transaction. This change would cascade up the Merkle tree to the Merkle root, changing the value of the Merkle root and thereby invalidating the block. So everyone can see that a Merkle tree allows a quick and easy test of whether a particular transaction is included in the set or not.

Why Merkle Trees are Necessary for Blockchain?

To understand how important Merkle trees are to blockchain technology, imagine a blockchain without them. We will cover Bitcoin mainly because using Merkle Trees is fundamental to crypto-currencies and easy to understand. For example, if Bitcoin did not have Merkle trees, every node in the network would have to maintain a complete copy of every transaction that ever happened on Bitcoin. Any authentication request on Bitcoin would require an extensive packet of data to send over the network, so you have to have it yourself to verify the data. The computer used for verification would have to use a lot of processing power to compare the ledgers to ensure that no changes have been made. Merkle Trees can effectively solve this problem. They hash records in the ledger, effectively separating the evidence of the data from the data itself. Proving the validity of a transaction only involves providing a small amount of information to the network. In addition, it allows you to demonstrate that both variants of the ledger are the same for the titular amount of computing power and network bandwidth.

Importance

Merkle Trees are vital because they allow Merkle proof. These allow us to quickly verify whether an input was included in a particular data set and in what order. Merkle Trees are also effective as they allow us to compress large datasets by removing all unnecessary branches while keeping the only ones we need to prove. In the blockchain world, this means that Merkle Trees provide the following critical features:

  • The ability to verify that a transaction is included in a block
  • Bright clients
  • Complete efficiency and scalability
  • Simplified payment verification

Advantages of Merkle Tree

  • Efficient Verification: Merkle trees offer efficient data integrity and validity verification and significantly reduce the amount of memory required for verification. Proof of verification does not require transferring large amounts of data over the blockchain network. Enable trusted cryptocurrency transfer in a peer-to-peer distributed system by quickly verifying transactions.
  • No Delay: There is no delay when data is transferred over the network. Merkle trees are widely used in the calculations that keep cryptocurrencies working.
  • Less Disk Space: Merkle trees take up less disk space compared to other data structures.
  • Unaltered Data Transfer: Merkle root helps ensure that blocks sent over the network are whole and unaltered.
  • Tampering Detection: The Merkle Tree offers an amazing advantage to miners in checking whether any transactions have been tampered with.
  • Since transactions are stored in a Merkle tree, which stores the hash of each node in the top parent node, any changes to the transaction details, such as the amount to be debited or the address to which payment must be made, will propagate to the hashes in the upper levels and finally to the Merkle root.
  • A miner can compare the Merkle root in the header with the Merkle root stored in the data part of the block and easily detect this manipulation.
  • Merkle-Tree search: O(logn), where n = number of transactions in a block.
  • Linked-List search: O(n), where n = number of transactions in the block.

Conclusion

Once you understand the basics of Merkle Trees, you can begin to appreciate the protection and efficiency of Blockchain data structures. If Merkle Trees had never been discovered, cryptocurrency and blockchain technology would never have existed. Without a suitable alternative, the amount of computing power and storage would be too expensive to operate. Merkel Trees are vital to blockchains and allow them to function while maintaining the integrity of transactions.

Key Takeaways:

  • Merkle Tree provides a means to maintain data integrity and validity.
  • Merkle Tree helps in saving memory or disk space as proofs, computationally easy and fast.
  • Merkle Tree evidence and management require a small amount of information transmitted over networks.
  • The Merkle root is a simple mathematical way to verify data on a Merkle tree.
  • Merkle roots are used in cryptocurrency to ensure that blocks of data passed between a peer-to-peer network are intact, uncorrupted, and unaltered.
  • Merkle roots are essential to the computation required to sustain cryptocurrencies such as Bitcoin and Ether.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

ANURAG SINGH CHOUDHARY
ANURAG SINGH CHOUDHARY

Passionate Machine learning professional and data-driven analyst with the ability to apply ML techniques and various algorithms to solve real-world business problems. I have always been fascinated by Mathematics and Numbers. Over the past few months, I have dedicated a considerable amount of time and effort to Machine Learning Studies.

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