Episode 32 How does machine learning algorithms helps UPIs like GPAY-PAYTM-AMAZON PAY
Episode 32 of the Tech Stories podcast, hosted by amit bhatt, titled "Episode 32 How does machine learning algorithms helps UPIs like GPAY-PAYTM-AMAZON PAY" was published on February 6, 2022 and runs 10 minutes.
February 6, 2022 ·10m · Tech Stories
Summary
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 pointsK-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”
Episode Description
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”