# Support Vector Machine(SVM):I can do both classification and regression.

You want to know what my real strengths are and also how I work so efficiently says SVM .

Before getting started to how I work. I want you to get acquainted with few terms and terminologies which will definitely help you in understanding me better. let’s get started….

# Performance Metrics for Classification problem .

Performance metrics are the way to understand how good the model is doing on the test data or on the validation data.

There are several metrics out there but not every metric can be used everywhere. So, one should have to know when he/she have to apply which metric even…

# Decision Tree Explained…

Decision Trees (DTs) are non-parametric supervised learning method used for classification and regression. The goal is to create the model based on some decision rules from data features. It is also capable of performing multi-class classification on the dataset.

Before telling you all about how decision tree algorithm works ,I…

# “I do address Over-fitting problem” says Lasso and Ridge Regression.

In this post ,I will tell you about “Why and when we use Lasso and Ridge Regression and their key difference.“

Suppose ,we have a best fit line where for one unit change of X ,Y is making a big change (Case of Steep Slope).In this case model can Over-fit…

# Linear Regression

Linear Regression insists that there is one (and only one )line that would characterize the trend and the relationships between the two variables.

## Linear Regression is a Machine Learning algorithm where we explain the relationship between a dependent variable(Y) and one or more explanatory or independent variable(X) using a straight line.

Here, the target class must be continuous feature and the features affecting the target class can be continuous or categorical .

Before directly applying Linear Regression ,one…

# What is Natural Language Processing?

Natural Language Processing or NLP is automated understanding of Human text or Speech.It is a special type of application of ML where we work with text data .

It is Extensively used in Sentiment analysis like understanding whether particular tweet or review is positive/negative.It …

ESTIMATION THEORY

Basically i ll be talking about characteristics of an estimator

UNBIASEDNESS

•Lets assume T_n is an estimator of some parameter θ from a population having density function

D(θ) .

If E(T_n )= θ for all θ∈θ(parameter space for θ), then T_n will be called an Unbiased estimator.

§… 