The Most Common Machine Learning Algorithms

The demand for data scientists and machine learning engineers has led to great competition for your first job in data science. It is believed that someone with a very good knowledge of the fundamentals of data science is more likely to get the job. Machine learning algorithms play a major role in the fundamentals of data science. There are so many machine learning algorithms to learn, but some of the algorithms are very common in all areas of data science. So in this article, I will introduce you to the most common machine learning algorithms that you need to learn.

The Most Common Machine Learning Algorithms

In every domain of data science, we are mostly dealing with the tasks of classification, regression, and clustering. So you should learn at least two algorithms for every task of machine learning. Below are the most common machine learning algorithms that you should know:

  1. Linear Regression
  2. Decision Trees
  3. Logistic Regression
  4. Naive Bayes
  5. K Means Clustering
  6. DBSCAN Clustering

So these were the most common machine learning algorithms that you should know. Now let’s go through all these algorithms one by one.

Linear Regression:

Linear regression is a statistical technique used as a Machine Learning algorithm to understand the relationship between a dependent and independent variable. This is a supervised learning algorithm that is used in regression based problem statements such as predicting future sales, future stock prices, etc.

It is a great algorithm for regression analysis, but it is not as powerful as other regression algorithms because it is affected by outliers because it assumes that the dataset is normally distributed without having any Outliers. So, whenever you use linear regression, make sure that the dataset is normally distributed without having any outliers.

Logistic Regression:

Logistic regression may look like another regression algorithm, but it is a classification algorithm. So, like linear regression, it also falls under the category of supervised learning but unlike linear regression, it is used for classification.

Logistic regression is one of the most powerful machine learning algorithms that can be used for binary classification problems such as classifying emails or SMS as spam or not spam.

Decision Trees:

A decision tree is an algorithm that predicts the label associated with an instance by travelling from a root node of a tree to a leaf. For example, we need to classify whether papaya is tasty or not, let’s see how the decision tree will express itself in this problem.

To classify whether the papaya is tasty or not, the decision tree algorithm will first check the colour of the papaya. If the colour is nowhere around pale green to pale yellow, then the algorithm will predict that papaya is not tasty without looking at more characteristics.

So what happens is we start from a tree with a single leaf and label it based on the majority of the labels we have in the learning set. Then we do a few iterations, with each iteration we see the effect of splitting the single sheet. Then among all the possible number of splits, either we select the one which maximizes the gain, or we choose not to select the sheet at all. This is how the decision tree algorithm works. You can learn how to implement it using Python from here.

Naïve Bayes:

Naive Bayes is a powerful supervised machine learning algorithm used for classification problems. It uses features to predict a target variable. The difference between Naive Bayes and other classification algorithms is that Naive Bayes assumes that the features are independent of each other and that there is no correlation between the features.

So what happens is that this hypothesis is not evaluated based on real-life issues. So, this naive assumption that features are uncorrelated is why this algorithm is known as Naive Bayes.

This algorithm is a classic demonstration of how generative assumptions and parameter estimates can simplify the learning process. In the Naive Bayes algorithm, we make predictions by assuming that the given characteristics are independent of each other. You can learn the practical implementation of this algorithm using Python from here.

K-Means Clustering:

The K-Means Clustering is a clustering algorithm capable of clustering an unlabeled dataset quickly and efficiently in just a very few iterations. It works by labelling all instances on the cluster with the closest centroid. When the instances are centred around a particular point, that point is called a centroid.

If you receive the instance labels, you can easily locate all items by averaging all instances for each cluster. But here we are not given a label or centroids, so we have to start by placing the centroids randomly by selecting k random instances and using their locations as the centroids.

 Then we label the instances, update the centroids, re-label the instances, update the centroids again and so on. The K-Means clustering algorithm is guaranteed to converge in a few iterations, it will not continue to iterate forever. You can learn to implement it using Python from here.

DBSCAN Clustering:

DBSCAN stands for Density-Based Spatial Clustering for Applications with Noise. This is an unsupervised clustering algorithm that is used to find high-density base samples to extend the clusters.

The DBSCAN Clustering algorithm is based on the concept of core samples, non-core samples, and outliers:

  1. Core Samples: The samples present in the high-density area have minimum sample points with the eps radius.
  2. Non-core samples: The samples close to core samples but are not core samples but are very near to the core samples. The no-core samples lie within the eps radius of the core samples but they don’t have minimum samples points.
  3. Outliers: The samples that are not part of the core samples and the non-core samples and are far away from all the samples.

The DBSCAN clustering algorithm works well if all the clusters are dense enough and are well represented by the low-density regions. You can learn its implementation using Python from here.

Summary

In this article, I introduced you to the most commonly used machine learning algorithms and their implementation by using the Python programming language. You need to learn all machine learning algorithms but to get your first job you should learn these algorithms perfectly as they are very common in almost all areas of data science. I hope you liked this article on the most common machine learning algorithms. Feel free to ask your valuable questions in the comments section below.

Default image
Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

Leave a Reply

Data Science | Machine Learning | Python | C++ | Coding | Programming | JavaScript