Machine learning “Top 10 Machine Learning Algorithms Every Data Scientist Should Know.”
Machine learning has penetrated every aspect of our lives, from the products we use to the services we use. It is an artificial intelligence (AI) subfield that allows machines to learn from data and improve their performance without being explicitly programmed. From speech recognition to self-driving cars, machine learning algorithms are used to solve a wide range of problems. In this article, we will go over the top ten machine learning algorithms that every data scientist should master as well as their real-world applications.
Linear Regression
Linear regression is a type of supervised learning algorithm that uses one or more input variables to predict a continuous output variable. It is frequently used to forecast housing prices based on characteristics such as square footage, number of bedrooms, and location.
Example
In the real estate industry, for example, linear regression can be used to predict the sale price of a home based on its features. To predict the sale price of a new house, a linear regression model can be trained using historical data.
2. Logistic Regression
Logistic regression is a supervised machine learning algorithm that uses one or more input variables to predict a binary output variable. It is commonly used to predict whether a customer will purchase a product or not.
Example
Logistic regression, for instance, can be used in the e-commerce industry to predict whether a customer will buy a product based on their browsing history and previous purchases. A logistic regression model can be trained on historical data to predict whether or not a customer will purchase a product from an online store.
3. Decision tree
Decision trees are a sort of supervised learning technique that uses one or more input factors to predict a categorical output variable. It is frequently used to forecast customer behaviour or to identify potential client categories.
Example
In the telecoms business, for example, decision trees can be used to discover possible consumer groupings based on usage patterns. To predict customer segments, a decision tree model can be trained using historical data.
Random Forest
Random forest is an ensemble machine-learning technique used to improve decision tree accuracy. It involves training multiple decision trees on different subsets of data and combining their predictions.
Example
In the healthcare industry, for instance, a random forest can be used to predict the likelihood of a patient having a specific disease based on their medical history. To predict the likelihood of a patient having a specific disease, a random forest model can be trained using historical data.
5. K-Nearest Neighbor (K-NN)
K-Nearest Neighbors is an unsupervised learning algorithm that identifies a data point's nearest neighbours. It is frequently used to identify comparable products or customers.
Example
In the retail industry, for instance, K-Nearest Neighbors can be used to identify similar products based on their features. To identify similar products, a K-Nearest Neighbors model can be trained using historical data.
6. K-Means Clustering
K-Means Clustering is a type of unsupervised learning algorithm that uses similarity to group data points. It is frequently used to identify customer segments.
Example
In the banking industry, for example, K-Means Clustering can be used to identify customer segments based on their behaviour. To identify customer segments, a K-Means Clustering model can be trained using historical data.
7. Support Vector Machines (SVM)
Support Vector Machines are supervised learning algorithms that are used to categorise data points into one of two groups. It is frequently employed in image recognition and text classification.
Example
Support vector machines, for example, can be used in the automotive industry to identify objects on the road, such as pedestrians or other vehicles. To identify objects on the road, a support vector machine model can be trained using historical data.
8. Naïve Bayes
Just like SVM, Naïve Bayes is a form of supervised learning technique used to categorise data points into one of two or more groups. It is frequently employed in text classification and spam detection.
Example
In the email sector, for example, Naive Bayes can be used to identify emails as spam or not spam depending on their content. To classify fresh emails, a Naive Bayes model can be trained using historical data.
9. Neural Networks
A neural network is a deep learning system inspired by the structure and function of the human brain. It's used to spot complicated patterns in data and then make predictions or conclusions based on those patterns. It is widely employed in image and speech recognition.
Example
In the healthcare industry, for instance, neural networks can be used to evaluate medical images and detect probable health risks. To assess new medical images, a neural network model can be trained using historical data.
10. Gradient Boosting
Gradient Boosting is an ensemble machine-learning technique used to increase decision tree accuracy. It involves successively training several decision trees, with each following tree correcting the faults of the prior tree.
Example
Gradient boosting can be used in the banking industry to forecast stock values based on historical data. To forecast future stock prices, a gradient boosting model can be trained using historical stock data.
Conclusion
To summarise, machine learning is a rapidly expanding field with numerous applications in a variety of industries. In this article, we have explored the top ten machine learning algorithms that every data scientist should master, as well as their real-world applications. Data scientists can construct more accurate and efficient machine-learning models by understanding these techniques and their applications. We hope that this article has helped you to have a better overview of machine learning and its potential to transform the world.