Top 12 Most Used Machine Learning Algorithms

With an increased demand for modern and reliable technology, machine learning (ML) and artificial intelligence (AI) have gained much popularity recently. Here is the list of the top eight most used machine learning algorithms in python.
With an increase in the demand for modern and reliable technology, machine learning (ML) and artificial intelligence (AI) have gained a lot of popularity. Nearly 77% of devices use machine learning facilities. ML is the application of AI that employs statistics for finding patterns in vast amounts of data sets. The platform that uses machine learning includes:

• Search assistants
• Voice assistants
• Social media feeds

The demand for machine learning online courses is increasing rapidly because of the wide array of career opportunities offered in this field.

What are Machine Learning Algorithms?

Machine learning algorithms in Python are the backbone of modern artificial intelligence, revolutionizing how computers learn and make intelligent decisions. These algorithms play a pivotal role in analyzing data, recognizing patterns, and making predictions without explicit programming. By understanding the key concepts of machine learning algorithms, their types, and their applications in Python, we can harness the full potential of AI and its transformative impact on various industries. Read more on Artificial Intelligence and Machine Learning Course and Data Science, Artificial Intelligence & Machine Learning course.

Machine learning algorithms in Python encompass a wide range of mathematical models that enable computers to learn from data. Unlike traditional programming, where tasks are explicitly coded, machine learning algorithms use patterns and data insights to perform complex tasks and make predictions. By training on labeled or unlabeled data, these algorithms can identify underlying patterns, make informed decisions, and classify data into specific categories. Explore more on Top 5 Advantage Of Online Machine Learning Course and How Long Does it Take to Learn Machine Learning?

Machine Learning Algorithms in Python

There are two primary types of machine learning algorithms: supervised learning algorithms and unsupervised learning algorithms.

Supervised learning algorithms in Python are trained on labeled data, where input features are paired with their corresponding output labels. During training, the algorithm learns to map inputs to the correct outputs, making it ideal for regression and classification tasks. Here is the list of the top eight most used machine learning algorithms in python:

1. Linear Regression

   Linear regression is one of the most supervised ML algorithms that observes features and predicts the outcome simultaneously. It is used for estimating absolute values based on the continuous variables.
   It is the most popular python ML algorithm and is often under-appreciated. The best fit line is called regression line and is represented by equation Y=a*X+b, where,

   • Y- Dependent Variable
   • a- Slope
   • X- Independent Variable
   • b- Intercept

   Linear regression is classified into two types:

   • Simple linear regression
   • Multiple linear regression

2. Logistic Regression

   It is a supervised classification that uses estimated discrete values like 0/1, yes/no, and true/false. It is based on independent variables. The logistic regression is used to predict the probability of an event and gives the value of output between 0 and 1.

3. Decision Tree

   A decision tree is the type of supervised learning algorithm that is used to predict classification problems. It uses both classification and regression. This model compares essential features with the determined conditional statements. A decision tree works on both categorical and continuous dependent variables.

4. Support Vector Mechanism (SVM)

   SVM is one of the essential machine learning algorithms in python that plots lines that divide different categories of the data. Here, we calculate the vector for optimizing the line, which helps to ensure that the closest point in each group lies far from the other.

5. Naive Bayes

   This classification is based on the Bayes theorem. It assumes independence between predictors and features in the class is unrelated to any other. Naive Bayes is easy to build and is helpful for large data sets. Thus, it is known to outperform comprehensive tasks effectively.

6. k- Nearest Neighbors

   This is a python machine learning algorithm that is used for classification and regression. K-NN is a simple algorithm that stores all the information and considers different centroids. Things to consider before selecting kNN:

   • It is expensive.
   • Variables should be normalized else higher range variables can bias it
   • Works on pre-processing stage more before going for k-NN

7. k- Means

   It is a supervised model that is used to solve clustering problems. The data are classified using several clusters.

8. Random Forest

   Random decision forests are used for various purposes like classification, regression, and other tasks. Tree vote provides a classification based on every new objective.

9. Support Vector Machines (SVM)

   A versatile algorithm used for both classification algorithms and regression, which separates data points using hyperplanes in a higher-dimensional space.

10. Hierarchical Clustering

    An algorithm that creates a tree-like structure of nested clusters, facilitating insights into the hierarchical relationships within the data.

11. Principal Component Analysis (PCA)

    A powerful dimensionality reduction technique that identifies the most significant features in the data, simplifying complex datasets.

12. Autoencoders

    Neural networks used for feature learning and data compression, particularly useful in anomaly detection and data reconstruction.

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Conclusion

Machine learning algorithms in Python find extensive applications across various domains, including image recognition, natural language processing, recommendation systems, and more. Python's rich ecosystem of libraries and frameworks, such as Scikit-learn, TensorFlow, and Keras, empowers researchers and developers to implement and deploy these algorithms effectively.

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