Top 5 simple and robust Machine Learning Algorithms

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In this article, you will learn-

As we know, Machine learning is growing very fast and many people are shifting their careers in ML, moreover, Harvard Business review article titled ‘Data Scientist’ the ‘Sexiest Job of the 21st century’.

As a beginner, you have a question in your mind what are The Top 5 Machine Learning Algorithm.

Therefore, Without wasting your time let’s just discuss The Top 5 Machine Learning algorithms-

Top 5 simple and robust Machine Learning Algorithms

Firstly, understand the types of Machine learning algorithms, before diving into actual algorithms

1. Supervised Machine Learning Algorithm- In a supervised machine learning algorithm, we have training data, which means we train our model on a labeled dataset. We have labeled data for training our model, we provide some new data to predict the result. It is also known as the Classification algorithm.
2. Unsupervised Machine Learning Algorithm- In an unsupervised machine learning algorithm, we don’t have training data, which means a model has to learn by itself without having any prior knowledge. It is also known as the Clustering algorithm.
3. Reinforcement Machine Learning Algorithms- Reinforcement learning use hit and trial, means model learns by its own mistakes and improves their accuracy, just like in video games.

Now we have covered types of machine learning, its time to know about Top 5 simple and robust Machine Learning Algorithms

1. Naïve Bayes Classifier Algorithm –

• It is very hard if you have to manually classify data such as documents, emails, or web pages, but Naïve Bayes Classifier does this task very easily. Naïve Bayes Classifier Algorithm is based on Bayes Theorem of Probability, which allocates the population’s element value from one of the categories that are available. Application of Naïve Bayes Classifier is spam filtering and sentiment analysis.
• If you have a large dataset then you should use Naïve Bayes Classifier.

2. Support Vector Machine Learning Algorithm–

• Support Vector Machine (SVM) is a supervised machine learning algorithm. It performs classification and regression problems tasks. In SVM, there is a hyperplane ( a line) which divides the data into different classes.
• SVM tries to find the hyperplane which maximizes the distance between different classes, and this term is called margin maximization that rises the chance of classifying the data more accurately.
• The application of SVM is stock market prediction.

3. K Means Clustering Algorithm –

• K means clustering algorithms divide each observation into a K cluster.
• It is unsupervised learning. It performs clustering. As it is a type of unsupervised learning so it starts with randomly selected centroids, it selects random K points which are centroids.
• A centroid is a virtual or exact point representing the center of the cluster.
• After that, it assigns every data to the closer centroid. You can use the Euclidean distance formula or use any other distance formula depends upon the distance type.
• K means clustering algorithm again calculate and locate the new centroid for each cluster.
• After that reassigning of data-point is performed based on the new closet centroid, if there any reassignment is performed then they repeat the previous step until the model is ready.
• K means clustering is an iterative process.

4. Apriori Algorithm-

• Apriori algorithm is based on association rules. For example, if someone who bought milk also bought bread. Another name of the Apriori algorithm is association rule learning.
• Marketing is the main application of the Apriori algorithm because it allows looking at certain patterns in buying items. For example, if someone buys milk and bread together so put milk and bread together in a supermarket to increase the sale of both items. By putting together both items, a person who comes to buy only milk might purchase bread too.
• Apriori algorithm work on three terms- Support, Confidence and lift.
  • Support (in terms of milk and bread Example) = customer who bought milk/ total no of customer
  • Confidence = customer who bought milk & bread both/ total customer
  • Lift = Confidence/ support

5. Decision Tree Algorithm-

• Decision Tree is used in two cases- Classification and Regression.
• As the name suggests Decision Tree, it is a tree-like structure start from the root node to the leaf node.
• The internal or non-leaf node of the decision tree represents the test on a feature, the leaf node act as a class label.
• The decision tree checks the condition in each node and proceeds down.

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