# Top 10 Algorithms Of Machine Learning For Beginners

Technology is highly dynamic, and the developments taking place in this field are enormous. Machine learning, which evolved a few years back, has gained traction after the recent Havard Business Review, which highlights how machine learning is going to change the course of future development in different fields. There are many institutes offering machine learning certification course. The course is open for both the beginners and the professionals.

ML algorithms derive experience from the data without human intervention. ML is an application of Artificial Intelligence (AI). Here we unfold what the ten best ML algorithms that you can learn.

### There Are Three Types Of ML algorithms

• Supervised learning – It makes use of labelled training data to learn mapping function. In simple words, it makes use of datasets and previous experience to map the output.

• Unsupervised learning â€“ It only has input variables and no output variables

• Reinforcement learning– It allows the agent to decide what should be the next course of action. It works on trial and error.

### 1. Linear Regression

As the name highlights, this ML algorithm established a relation between the independent and dependent variable by organizing them in line. It is depicted using the following equation: Y= a *X + b.

Here

• Y â€“ Dependent Variable
• a â€“ Slope
• X â€“ Independent variable
• b â€“ Intercept

### 2. Naive Bayes

This algorithm is used to calculate the probability that an event will take place while the other event has already taken place. In this algorithm, we make use of Bayesâ€™s Theorem. This algorithm is called naĂŻve because all the variable is independent. We test a hypothesis using the following equation :

P(h|d)= (P(d|h) P(h)) / P(d)

Here:

• P(h|d) = Posterior probability
• P(d|h) = Likelihood of a hypothesis to be true
• P(h) = Class prior probability
• P(d) = Predictor prior probability

### 3. Logistic Regression

Unlike linear regression, this ML algorithm makes used for discrete variables. This method is used to predict the probability of an even but fitting the variations in logit function.

### 4. Decision Tree

This is yet another popular algorithm for machine learning. It is a part of supervised learning and works on dividing the data into homogenous groups based on the independent variable. It classifies continuous dependent on variables and categorical variables.

### 5. Recurrent Neural Network or RNN

Did you know that Siri and Google Assistant work on Recurrent Neural Network or RNN. It works similar to a neural network which has nodes in it. These nodes have all the information/memory, which makes sequential data processing easy and simplified. Let’s understand how RNN is different from the normal neural network if the word finance has to be processed character by character, then in case of the normal neural network, by the time we move from F to I, the node forgets F, but this is not the case with RNN.

### 6. KNN or K nearest Neighbour

This algorithm works on separating the data into different classes based on their similarities. This is the simplest algorithm which stores all the cases and based on this stored data, it classifies the new cases by taking into account the majority vote of its k neighbours.

### 7. K-Means

Itâ€™s a kind of unsupervised ML algorithm which works on clustering the data. In this algorithm, the data sets are distributed into clusters, and each cluster has homogeneous data, but it is heterogeneous to the other cluster. How K-means forms clusters?

To perform this algorithm:

• It picks centroid from each cluster called as K.
• Now each data point forms a cluster which is nearest to K clusters
• Now centroids are formed using the current cluster member
• The new centroids help in determining the closest distance for each data point. This process takes place until the time the centroids donâ€™t change.

### 8. Apriori

It is an unsupervised ML algorithm which works on the If_Then format. It means that if A occurs then there is a probability of B to take place. In simple words, it generates association rules where the two events A and B are associated and the occurrence of A impacts the occurrence of B. For example, if people will buy a mobile phone, then they will buy mobile covers as well.

This makes use of different models to come to a decision, but it gives weighable based on their accuracy in unveiling the output.