An Algorithm is a series of steps that helps in solving specific problems. Machine Learning Algorithms helps us in providing mathematical equations which help in predicting the future by using the past behaviour of the data. There is no algorithm which will help in every task of machine learning, so we have so many algorithms that can be used according to the task we are working on. Here, In this article, I will take you through the 7 most powerful and mostly used machine learning Algorithms.

**The Mostly Used Machine Learning Algorithms**

**Linear Regression**

Linear Regression is one of the easiest and one of the most popular Supervised Machine learning algorithms. It is a common statistical tool for modeling the relationship between some “explanatory” variables and some real-valued outcome. It predicts by simply computing a weighted sum of the input features, plus a constant called the bias term. You can learn to apply a Linear Regression Algorithm from **here**.

**Logistic Regression**

Just like a Linear Regression algorithm, the Logistic Regression Algorithm is also one of the most popular Supervised Machine Learning Algorithms. Logistic Regression models are mostly used in the problems of Binary Classification. One of all the machine learning algorithms a logistic regression has provided over more than 95 per cent accuracy most of the times in the tasks of Binary Classification. You can learn to implement this algorithm from **here**.

**Decision Trees**

Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks and even multi-output tasks. They are powerful algorithms, capable of fitting complex datasets. Decision trees are also the fundamental components of Random Forests, which are among the most powerful algorithms available today. You can learn to implement this algorithm from **here**.

**Support Vector Machines**

Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. The Support Vector Machines algorithmic tackles the sample complexity challenge by searching for “large margin” separators. Roughly speaking, a halfspace separates a training set with a large margin if all the examples are not only on the correct side of the separating hyperplane but also far away from it. You can learn to implement this algorithm from **here**.

**Naive Bayes**

In Machine Learning Naive Bayes models are a group of high-speed and simple classification algorithms that are often suitable for very high-dimensional datasets. Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem. The Naive Bayes algorithm is a classical demonstration of how generative assumptions and parameter estimations simplify the learning process. You can learn to implement this algorithm from **here**.

**K-Means**

Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as k-means clustering, which is implemented in sklearn.cluster.KMeans. The *k*-means algorithm searches for a pre-determined number of clusters within an unlabeled multidimensional dataset. You can learn to implement the K-means algorithm from **here**.

**Random Forest**

The Random Forest algorithm is an ensemble of the **Decision Trees algorithm**. A Decision Tree model is generally trained using the **Bagging Classifier**. If you don’t want to use a bagging classifier algorithm to pass it through the Decision Tree Classification model, you can use a Random Forest algorithm as it is more convenient and better optimized for Decision Tree Classification. You can learn the implementation of this algorithm from **here**.

I hope you liked this article on the regularly used machine learning algorithms. Feel free to ask your valuable questions in the comments section. You can also follow me on **Medium** to learn every topic of Machine Learning.