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In this blog we will start with a short introduction to NLP and its applications and further a brief overview of techniques in NLP is given.


Natural Language Processing (NLP) deals with computer-human interaction using natural language. It is a branch of AI.
The ultimate goal of NLP is to translate, decipher, comprehend, and make sense of human languages in a useful manner. NLP can be dealt with text or speech at the end they are converted to numbers for processing with different techniques.

We see NLP applications in our day to day life. Chat bots, Siri, Alexa, Grammarly, Goggle…

This is one of quite interesting techniques of clustering in unsupervised machine learning. Let us start to know more about the algorithm involved in it and implementation.

What is Clustering??

Clustering is a strategy for grouping similar objects together such that the objects in the same category are more similar than the objects in other groups. A Cluster is moreover a set of related items.

Types of Clustering :

  1. K-Means Clustering
  2. Hierarchical Clustering
  3. Mean-Shift Clustering
  4. Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  5. Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

In this blog we will concentrate only on hierarchical…

Did you ever think how are kids able to learn ? Come on !!… think now……… I hope you might guessed it right, its all about observations and experiences.

In simpler words we can say that we are surrounded by humans who can learn something from their experiences thanks to their learning abilities, where computers and machines follow our commands. But, like a person, can a computer learn from past experiences or data? And that’s where Machine learning comes in.

In machine learning, we do not explicitly code machines on how to solve a particular problem. Rather than that, we…

Are you new to hypothesis testing and wonder what it is ? Then this blog is for you as I tried to make it self explanatory with simple examples.

Table of Contents

  1. Hypothesis and Hypothesis testing
  2. Null and Alternative Hypothesis
  3. Some important concepts — Significance level, Critical region, Critical Value, P-Value, Types of errors (Type-1 Error, Type-2 Error).
  4. Steps to follow for testing a hypothesis
  5. Test stastic for single and two normal populations (i) Sample mean (ii) Sample Variance (iii) Sample proportion

What does hypothesis and hypothesis testing mean ?

We generally encounter lot many problems and they are to be either accepted or rejected and such a problem statement is…


Data science trainee at Almabetter

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