Hi, I am up with a new blog for this week. In this blog I will be talking about NLP(Natural Language Processing) what it is, and various techniques revolving it.

NaturalLanguageProcessing

Natural Language Prcessing or NLP in short form commonly is an extended ability of our system(Any programming languages) to intrepret any unstructured data like text,image,audio etc and analyse them to get insight. The term “language” in NLP is not just restricted to human text, but it covers all kind of human, animal communications.

  • Google searches: We enter a given keyword and google search engine internally intrepret the users input and…

It comes under the gambit of Unsupervised learning- a branch of Machine learning mainly used for finding the pattern in data where the target variable is not known or yet to be discovered. This technique is usually applied before building a model for the problem statement. In Clustering the major task is to divide the population or data points into various groups such that data points in the same groups are more similar to other data points which are in the same group than those in other groups. In simple terminology, the objective is to segregate groups with similar characteristics…


Linear Regression

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. … Hence, the name is Linear Regression.

The idea behind Linear Regression model is to obtain a line that best fits the data. By best fit, what is meant is that the total distance of all points from our regression line should be minimal. Often this distance of the points from our regression line is referred to as an Error though…


Need For Machine Learning

Ever since the technical revolution, we’ve been generating an immeasurable amount of data. As per research, we generate around 2.5 quintillion bytes of data every single day! It is estimated that by 2021, 1.7MB of data will be created every second for every person on earth.

With the availability of so much data, it is finally possible to build predictive models that can study and analyze complex data to find useful insights and deliver more accurate results.

Top Tier companies such as Netflix and Amazon build such Machine Learning models by using tons of data in order to identify profitable…


Data must be interpreted in order to add meaning.

We can interpret data by assuming a specific structure our outcome and use statistical methods to confirm or reject the assumption. The assumption is called a hypothesis and the statistical tests used for this purpose are called statistical hypothesis tests.

Whenever we want to make claims about the distribution of data or whether one set of results are different from another set of results in applied machine learning, we must rely on statistical hypothesis tests.

The main purpose of statistics is to test a hypothesis. For example, we might run an…


As Josh Wills once said, “Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” .

Hi Guys, my name is Shirsh and I am a aspiring Data Scientist ,currently I am building my skill sets to excel in this domain , in this blog I will be summarizing all important Statistics Concept which I have learnt till date.

Doing this will help me build my concept better and would also help guys who are new to this field.

So let’s get started

What do I mean when I say what is Probability?

1.There is 50% chance of rains tomorrow evening

2. There in .33 probability of you failing

Probability in simple words means : Unable to predict the outcomes, but in the long-run, the outcomes exhibit statistical regularity.

Example:

Tossing a fair coin — outcomes S ={Head, Tail} Unable to predict…

Shirsh Verma

Data Science Trainee at Almabetter

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