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12 Most Used Machine Learning Algorithms in Python

Author Gunjan Batra
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Introduction

With the advent of automation and Artificial Intelligence (AI), we have gotten incredibly dependent on automated services and machine assistance. Machine Learning(ML) is one of the fundamental methods developers and data scientists use to power these machines, systems, and services.

ML also helps solve many business problems and mathematical challenges which can be time-consuming and ineffective if humans do it manually. ML allows machines to predict outcomes and figure out values that we are not aware of by accurately going through datasets, historical data and other variables.

Learn about machine learning algorithms

With effective Machine Learning algorithms, ML can promote discovering realistic or accurate solutions to problems even when working with bad quality data or with changing values.

What are Machine Learning Algorithms?

Machine Learning algorithms are behind making machines and systems smarter on their own and fundamentally acquiring the ability to learn. Machine learning algorithms are programmed mathematics and logic that adjusts themselves to learn from data and give better performance.

ML algorithms shape the framework and lay out the foundations behind how the machine or system will function. Machine Learning algorithms power automated systems and many AI integrations. Algorithms expressed in mathematical forms will have an equivalent in most programming languages.

Similarly, Machine Learning algorithms can be used across multiple sets of tools using a lot of programming languages. However, some programming languages are better than others at ML and handling ML tasks.

There are two main types of Machine Learning algorithms. Namely,

Supervised Learning Algorithms

These algorithms help with practical ML problems and use both the variables, input variables, and output variables. These algorithms help systems learn to determine similar output as required from the dataset provided by comparing the results with an already given output and then effectively predict the output better each time through learning how to map the function better by taking reference from the given output. The machine is successful once it produces the same result on its own. 

Unsupervised Learning Algorithms

These algorithms only use input variables as output variables are not given. These algorithms help the machines learn from the dataset on their own through modelling the data structure and are not supervised by humans. These machines are tasked with discovering information from the given datasets and are also programmed to solve association and clustering problems.

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12 Most Used Machine Learning Algorithms in Python

There are many Machine Learning algorithms that are used to provide machines with the ability to learn or determine the required information or insights from datasets. Here is the list of the top 12 most used Machine Learning algorithms which are extensively used to power ML by using Python.

  • Naive Bayes
  • Decision Tree
  • Random Forest
  • Apriori Machine Learning Algorithm
  • Logistic Regression
  • Linear Regression
  • K-nearest Neighbors or KNN
  • Support Vector Machines or SVM
  • Discreet Hopfield Network
  • Back-propagation
  • XGBoost
  • Hierarchical Clustering
     

Let’s discuss all the ML Algorithms used in python in detail. 

Naive Bayes

This algorithm is based on Bayes’ theorem and consists of a classification method which functions by assuming that the features inside a class are not affected by other features inside the same class. The algorithm assumes that the features have no relation with each other, even if they are dependent on each other. This algorithm offers a model which works really well with massive datasets.

Decision Tree

It is one of the most popular supervised Machine Learning algorithms out there, being used for both classification and regression problems. Decision tree functions by navigating the complete tree and comparing the features with the help of conditional statements. The decision tree runs on both categorical dependent variables and continuous dependent variables.

Random Forest

It fundamentally represents an ensemble learning method for classification, regression and other problems that function by building a compilation of many decision trees during training time. Random Forest classifies objects based on attributes, and each decision tree is given a class. This algorithm then chooses the class which reports the highest number of trees.

Apriori Machine Learning Algorithm

This algorithm is a categorisation algorithm and is used when one needs to sort massive amounts of data. This algorithm can be given a dataset to generate association rules and then used to track developments of the item sets to build categories. It is an unsupervised ML algorithm that can be used to predict occurrences and for the auto-completion of processes.

Logistic Regression

It can be used to predict independent values such as In or Out, 0 or 1 and Negative or Positive. It is one of the most popular supervised ML algorithms, which uses a logistic function to determine the output as either 1 or 0. This is a classification algorithm that follows a statistical model to determine the probabilities of classes or occurrences.

Linear Regression

It helps in predicting an outcome while observing independent features. This ML algorithm helps establish a linear relationship between independent variables and dependent variables. This fundamentally means that it shows how the dependent variable is affected by the value of the independent variables.

K-nearest Neighbors or KNN

It can analyse the labels of given data points surrounding target data points and make predictions to classify the data points. KNN is used for both regression and classification tasks. It is a supervised learning algorithm that is used to recognise patterns, mine data and detect abnormalities.

Support Vector Machines or SVM

It differentiates various categories in the dataset and then effectively optimises this differential line through vector calculation. This is a supervised ML algorithm that helps in analysing data for regression and classification problems.

Discreet Hopfield Network

It helps create an artificial neural network that stores information and can recall this information from partial input. This algorithm offers machines a recurring behaviour that can be termed auto-associative.

Back-propagation

This algorithm is able to design given functions by altering the weights of the input signals, thus producing the required output signals. This is a supervised learning algorithm that is used for classification and regression. Back-propagation finds the minimum values of error functions through gradient descent or the delta rule technique. This is how the algorithm discovers the required weights that will minimise or neutralise error functions.

XGBoost

It is a gradient-boosting algorithm that is used for various functions such as regression, ranking and classification. This contains the tree learning algorithm and the linear model both, which allows XGBoost to predict events with high accuracy. This algorithm is comparably about 10 times faster than prior gradient boosting techniques.

Hierarchical Clustering

This algorithm follows a method of cluster analysis. It functions by putting similar objects into groups called clusters after developing a cluster tree to represent the data. These groups, also known as nodes, are interconnected with two or more successor groups, and each node is allotted data that is similar in nature.

Also read - Prims and Kruskal Algorithm

Frequently Asked Questions

Which is the best machine learning algorithm used in Python?

Linear Regression can be declared as being the best ML algorithm in Python as it is extensively used for ML and preferred by many ML developers. This supervised ML algorithm is popular and acclaimed for predicting outcomes while observing features. It is capable of running on single variables or multiple variables. It is also relatively less complex and easier to apply; hence, it is preferred by a lot of beginners as well.

What algorithms are used in machine learning in Python?

Popular algorithms in Python for machine learning include Linear Regression, Decision Trees, Random Forest, Support Vector Machines, K-Means Clustering, and Neural Networks.

What are the 4 types of machine learning algorithms?

The four types of machine learning algorithms are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Which algorithm is best in ML?

The "best" machine learning algorithm depends on the problem domain, data characteristics, and performance metrics. Common choices include Random Forest, Gradient Boosting Machines, and Neural Networks.

Which ML algorithm is faster?

The speed of machine learning algorithms varies based on factors such as dataset size, algorithm complexity, and hardware specifications. Algorithms like Naive Bayes and Linear Regression tend to be faster.

How does ML algorithm work?

ML algorithms work by learning patterns and relationships from data, using techniques such as supervised learning with labeled data, unsupervised learning to find hidden patterns, and reinforcement learning through trial and error.

Conclusion

There are many Machine Learning algorithms that help developers and data scientists build machines and systems that are allowing organisations and companies to accomplish various IT, business and statistical goals. When choosing the right ML algorithm, one must consider the environment that he or she is working in and the immediate as well as the future requirement of the project.

Recommended Reading -

ML projects can demand immediate results or solutions to problems, and many algorithms can take various approaches to the same problem. It also does not depend on languages as most of these will work across the common programming languages.

Algorithms are fundamentally the same across all languages as they all originate from mathematics, so when choosing ML algorithms that can serve the same purpose, it completely depends on the user preference and language of choice.

However, when it is not about solving problems and machines need to be programmed into learning through data, user behaviour and real-time use, fundamentally functioning on its own, specific algorithms are best suited for these purposes. 

You can also consider our Machine Learning Course to give your career an edge over others.

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