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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

Want to learn Blockchain? Start Your Journey Here!

Amrutha
Amrutha Last Updated : 23 Jun, 2022
5 min read

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

Introduction on Blockchain

In this article, we will learn about blockchain, what is blockchain, how transactions are made, and how bitcoin transactions are made. Nowadays we are hearing about many attacks on computer system security. There is no guaranteed security. This is where blockchain comes into the picture. Blockchain provides you with the highest security. In this article, we will also see what makes blockchain so secure. Nowadays blockchains are very popular.

Blockchain mainly deals with data security. Blockchain technology can be the future of data science. The handling of data can be possible using blockchain. If the quantity of data is represented by big data then we can say blockchain is the quality of that data. Blockchain ensures data integrity. When it comes to data analysis it is difficult to monitor the changes but with the help of the blockchain’s distributed nature, all changes can be monitored. On one side data science is all about utilizing the data whereas blockchain is all about providing security to it.

Let’s get started.

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Blockchain

Blockchain is described as the collection of records linked with each other and they are strongly resistant to alteration and these are protected using cryptography. By the name itself, it suggests that blockchain is the chain of blocks that contains information. Once the data is stored inside the block it is very difficult to change it. To complete transactions without having to deal with banks, online wallets, and other third-party applications in an easier way blockchain concepts are used. Now let us see what actually block contain.

A block contains mainly three parts.

1. Data

2. Hash

3. Hash of the previous block

Blockchain

Data: Based on the type of blockchain, the data that is stored in the block depends. For example, take the example of the bitcoin blockchain, here the data contains the details of the transaction including the details of the sender, receiver, and the number of bitcoins.

Hash: Hash identifies the block and the contents inside the block. Every person has a unique fingerprint. Similarly, a block has a unique hash value. Hash is unique as a fingerprint. No two blocks have the same hash. When a block is created, the hash is created that has a unique value. And hash is a unique value so any changes in the block result in a change in the value of change. Hence, it is secured.

Hash of the previous block: Every block contains the hash value of the present block and the hash value of the previous block. So if there is a change in any one block then the hash value present in that block also changes which is different from the hash value of the previous block present in the next block which makes the entire blockchain invalid.

The use of only hashing techniques does not keep the blockchains safe from tampering. Nowadays computers are faster and are able to calculate hundreds and thousands of hashes per second. So the attacker can tamper with the block and can recalculate the hashes of other blocks in seconds which makes the blockchain valid. To deal with it blockchains use something called proof-of-work.

Proof-of-work

Proof-of-work is the mechanism in the blockchain that slows down the creation of blocks. Let us take the case of bitcoins. For the bitcoins case, it takes about 10 minutes to calculate the required proof-of-work and to add a new block to the chain. This mechanism makes it harder for the attackers to make an attack with the blocks. Therefore, for a blockchain, the security comes with the combination of hashing and proof-of-work.

New Era in Technology: Blockchain Traceability | VISIOTT

Crypto Currency

When the bank transactions are done, they could fail many times and the reasons may be due to technical issues from the bank side, account hacked by the attacker, daily transfer limit, or transfer charges. To solve these kinds of issues cryptocurrencies came into the picture.

Cryptocurrencies are the form of digital currency. They don’t exist. Ther are virtual currency. The fake currency problem can be avoided by using cryptocurrency. It doesn’t require any central authority and these are protected by strong and complex encryption algorithms which are very hard to tamper with. In the market, there are hundreds and thousands of cryptocurrencies and among them, BITCOIN is in the first position.

Bitcoin

Bitcoin is a type of digital currency which is independent of the central bank where records of all the transactions are maintained. Let us understand how bitcoin transactions are made by taking one example.

Imagine there are two persons and each has 5 bitcoins. Now if a person1 sends 2 bitcoins to person2 then finally person1 has 3 bitcoins and person2 has 7 bitcoins. In this way, data is updated after every transaction. When person1 sends 2 bitcoins to person2 then the record is created in the form of a block. This block contains the details of the transaction. Record also has the number of bitcoins each has. If there are multiple persons then multiple records are created after every transaction and all these blocks are linked to each other. So it is called the blockchain. This chain of records is called a ledger and this ledger is shared among all the persons. Here hackers cannot alter the blockchain because each user has a copy of the ledger.

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Bitcoin Transaction

In the bitcoin network, there are multiple users and each user in the network has two keys. The two keys are the public key and the private key. The public key has an address that everyone in the networks knows what it is. And Private key has a unique address that only that particular user knows.

When person1 wants to send 2 bitcoins to person2 then the wallet address of the person1 is hashed with his private key and now it’s encrypted and digitally signed. Now it is becoming the public key of person 2 and now person2 can decrypt it using his private key.

Different cryptocurrencies use different hashing algorithms.

Bitcoin uses the SHA256 algorithm.

Ethereum uses the ETHASH algorithm.

Advantages of Blockchain Technology

1. This blockchain technology is accessible to anyone without any permission.

2. The data present in it is verifiable by zero-knowledge proof.

3. All the data is permanently stored.

4. Data present in it cannot be tampered with by the attacker.

5. No third parties are involved during the entire process.

Disadvantages of Blockchain Technology

1. Here size of the blocks is fixed and hence it cannot be scaled.

2. Blockchain technology is a newly evolved technology and so it cannot be trusted.

3. Lot of energy is needed for the verification of transactions

4. Adding blocks is a time taking process.

5. Some countries have banned blockchain technology.

Conclusion

The creation of blockchain has peaked a lot of people’s interests. This blockchain technology can be used for some applications like storing medical records, creating a digital notary, and even collecting taxes. This blockchain technology is very useful as it provides high standards of security. Overall in this article, we have seen

  • Relation between Data Science and Blockchain
  • Introduction to blockchain
  • Bitcoin cryptocurrency, how bitcoin transactions are made
  • Advantages and disadvantages of blockchain technology

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

Amrutha
Amrutha

This is Amrutha, I am pursuing B.Tech in the Computer science Department. I am interested in developing ML Models with python and Data Analysis. And also I have an interest in Web Development. I hope my articles in Analytics Vidhya help you to learn better. Thank you!!

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