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**Advanced Machine
Learning** **Algorithms You
Need To Know**

In the present 21^{st} century, technology has
come across a long way to such an extent that most of the complex &
monotonous manual tasks have become automated. Automation of resources which
was previously considered to be a challenging task has now become a lot
simplified & much smarter in terms of application. This has become possible
with the advent of Machine Learning algorithms which can present machines with
the ability to perform tasks on their own

Ever seen computer play chess or a robot performing surgery? All these are the application of Machine Learning algorithms.

Now let’s have a look at some of the most complex & extensively used Machine Learning algorithms

**Linear Regression**

The concept of Linear Regression is all about visual analysis. For better understanding this concept let’s imagine the case where we need to arrange random persons in a straight line in increasing order of their weight. The point here is that you cannot weigh each person. You just have to visualize & arrange them accurately based on you visual parameters. This is what linear regression is like.

Linear Regression establishes a relationship between independent and dependent variables by fitting them to a line. This line is known as regression line and represented by a linear equation Y= a *X + b.

In this equation:

Y – Dependent Variable

a – Slope

X – Independent variable

b – Intercept

**Logistic Regression**

The concept of Logistic Regression is to estimate discrete values (usually binary values like 0/1) from a set of independent variables. If you want to accurately predict the occurrence of any event from a set of events then Logistic Regression is apt for the situation. By fitting data to a logit function it predicts the occurrence of an event. It is also called logit regression.

Here below are a few methods the can help in improving the accuracy in logistic regression models:

- Include Interaction Terms
- Eliminate Features
- Regularize Techniques
- Use A Non-Linear Model

**K-Means**

To solve complex clustering problems this is the perfect algorithm. This is an unsupervised model that classifies data sets into a particular number of clusters (let’s call that number K) This classification is carried out in such a way that all the data points within a cluster are homogenous and heterogeneous from the data in other clusters.

Apart from these, other extensively used algorithms in Machine Learning domain are Random Forest, Dimensionality Reduction Algorithms, Gradient Boosting & AdaBoost, Naive Bayes, SVM (3.Support Vector Machine), Decision trees & such

Get to know more about these prominent Machine Learning algorithms by being a part of Analytics Path **Machine Learning Training In Hyderabad **program**. **