Tech Stories

In this episode I covered the USE CASE OF UPI- TRANSACTION 

What is Clustering?

Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as "A way of grouping the data points into different clusters, consisting of similar data points

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. ... In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.

You can think of an N-gram as the sequence of N words, by that notion, a 2-gram (or bigram) is a two-word sequence of words like “please turn”, “turn your”, or ”your homework”, and a 3-gram (or trigram) is a three-word sequence of words like “please turn your”, or “turn your homework”

In this episode ,I tried to explain the concept of Crypto Currenct based NFT 

NFT: NFT (Non Fungible Token) are pieces of Digital content that are registered on the blockchain network and graphically represent underlying cryptocurrency that used to buy or register it.

 NFT stands for non-fungible token, which basically means that it's a one-of-a-kind digital asset that belongs to you and you only. The most popular NFTs right now include artwork and music, but can also include videos and even tweets 

Non Fungible Token: “Non-fungible” more or less means that it’s unique and can’t be replaced with something else 

Fungible  Token: Bitcoin and other cryptocurrencies are the most prominent uses of blockchain technology, and they're examples of fungible tokens. If you send somebody a Bitcoin and they send you one back, it doesn't have to be the same Bitcoin, just as with the ten-rupee note. Do 

In this episode I narrate the story of recommendation system used by OTT Platforms, Social Medias and Video Platforms like Youtube

Recommendation System

How does OTT recommendation work?

Custom recommendation system analyzes the past data history of a user and predicts the future insights that are more likely to engage the user.

Cloud-based recommender systems help the OTT or VOD service providers in better understanding whether a service satisfies the user requirements or not.

Which recommendation system next flix use?

The Netflix Recommendation Engine

Their most successful algorithm, Netflix Recommendation Engine (NRE), is made up of algorithms which filter content based on each individual user profile.

The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences.

What is  Content-based filtering?

Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback

In This episode I have covered step by step process 

How to Build Chat Bot in 5 Mins?

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In this episode I covered the fundamental of Natural language processing 

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written -- referred to as natural language. It is a component of artificial intelligence

NLP consist of two things 

Data PreProcessing:Data preprocessing involves preparing and "cleaning" text data for machines to be able to analyze it

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Happy New year!!!

Every year we commit for new resolution to ourself, How deep learning measure the accuracy, precision , senstivity of them

In this episode , I covered following performance metrics of Machine learning

 Confusion Matrix:It is the easiest way to measure the performance of a classification problem where the output can be of two or more type of classes. A confusion matrix is nothing but a table with two dimensions viz. “Actual” and “Predicted” and furthermore, both the dimensions have “True Positives (TP)”, “True Negatives (TN)”, “False Positives (FP)”, “False Negatives (FN)”

It is most common performance metric for classification algorithms. It may be defined as the number of correct predictions made as a ratio of all predictions made. We can easily calculate it by confusion matrix with the help of following formula −

Accuracy=TP+TN/TP+FP+FN+TN

Precision, used in document retrievals, may be defined as the number of correct documents returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

Precision=TP/TP+FP

Recall may be defined as the number of positives returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

Recall=TP/TP+FN

Specificity, in contrast to recall, may be defined as the number of negatives returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

Specificity=TN/TN+FP

This score will give us the harmonic mean of precision and recall. Mathematically, F1 score is the weighted average of the precision and recall. The best value of F1 would be 1 and worst would be 0. We can calculate F1 score with the help of following formula −

𝑭𝟏 = 𝟐 ∗ (𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏 ∗ 𝒓𝒆𝒄𝒂𝒍𝒍) / (𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏 + 𝒓𝒆𝒄𝒂𝒍𝒍)

F1 score is having equal relative contribution of precision and recall.

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Its our first anniversary !!!

I tried very hard to acheive this success. Thanks alot audience

कोशिशे कभी नाकाम नहीं होती वादें अक्सर टूट जाते है

क्योंकि

लहरों से डर कर नौका पार नहीं होती,

कोशिश करने वालों की कभी हार नहीं होती।

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Zindagi ek daud hai aur hum sab log bhagne ki koshish mai lage hai kyonki sabko safalta chahiye

Chaliye saath he bharte hai udaan

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In This Episode I covered the following things

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In this episode I tried to explain the principle behind face regonition using PCA -Eigen face Approach PCA- Principal Component Analysis Eigen faces:  An eigenface is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. What is eigenvalues and eigenvectors in PCA? Eigenvectors are unit vectors with length or magnitude equal to 1. ...  Eigenvalues are coefficients applied to eigenvectors that give the vectors their length or magnitude.  Covariance Matrix:is a square matrix giving the covariance between each pair of elements of a given random vector.

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