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8 Best Machine Learning Algorithms

The best machine learning algorithms data scientists and machine learning engineers should know.

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Artificial intelligence emerged with the question of whether machines can think like humans. AI is one of the hottest fields of study today. Machine Learning is a subset of AI that provides a machine the ability to learn automatically from experience without being explicitly programmed. Many problems that could not be solved with classical statistical methods could be solved with machine learning algorithms.

In this post, I’ll talk about the 8 best machine learning algorithm families. I’ll cover the following topics:

  • Regression algorithms
  • Ensemble learning algorithms
  • Clustering algorithms
  • Dimension reduction algorithms
  • Bayesian algorithms
  • Association rule algorithms
  • Artificial neural networks algorithms
  • Deep learning algorithms

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What is Machine Learning?

Before diving into algorithms, I’m going to briefly talk about what machine learning is. Machine learning helps to find hidden patterns in the dataset. There are four types of machine learning algorithms. Let’s see them quickly.

Supervised learning

Supervised learning techniques are used for labeled data. Most machine learning algorithms use supervised learning. Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships that it learned from the previous data sets

Unsupervised learning

Unsupervised learning techniques are used for unlabeled data. In this technique, the system tries to find the hidden information in the data without a teacher. In other words, in unsupervised learning, meaningful information in the data is revealed without an output variable.

Semisupervised learning

You can use semisupervised learning algorithms if some data have labels and some do not. A good example of semisupervised learning is image data. In image data, some images usually have tags, such as dogs or cats, and some do not.

Reinforcement learning

In this technique, the aim is to develop a learning system that improves the decision-making process by communicating with the environment. An action is chosen and implemented in exchange for a reward or punishment. Thus, the best strategy is learned with rewards and penalties. The most popular example of reinforcement learning is chess. The system decides the best place for the chess piece. At the end of the game, the strategy is rewarded as winning or losing. Many robots use to learn how to walk using this technique

Now, let’s take a look at the 8 best machine learning algorithms.

Machine Learning Algorithms

Machine learning algorithms are a huge part of machine learning. You can use a machine learning algorithm to find hidden patterns in a dataset. When analyzing data, you should start with simple algorithms such as regression. If you can’t obtain a good model, you should use more complex models.

1- Regression Algorithms

The most widely used and easy to explain machine learning algorithms are regression algorithms. Regression algorithms are a supervised learning technique. You can train regression algorithms to find the relationship between the input and output variables in the data set. You can use linear regression if your target variable is numerical, and logistic regression if it is categorical. In addition, if your model has an overfitting problem, you can think of regularization techniques. For example, I can use ridge, lasso, and elastic net as regularization algorithms.

2- Ensemble Learning Algorithms

If your target variable is categorical or numerical, you can use ensemble learning algorithms. Ensemble learning algorithms were developed to overcome the memorization problem in decision trees. For example, you asked thousands of people a complex question and gathered the answers. In many cases, these put-together answers may be better than an expert’s opinion. This is called the wisdom of the crowd. Just like this, if you combine the estimates of a group of estimators, you can predict better than the best estimator. The group of predictors is called ensemble learning. Solutions based on ensemble learning algorithms have won most of the competitions held on Kaggle. The most used ensemble learning algorithms are random forests and XGBoost techniques.

3-Clustering Algorithms

Clustering algorithms are used for unlabeled data, so they are unsupervised learning techniques. These algorithms find similar relationships and patterns within the dataset and group data with similar characteristics. The most used clustering techniques are k-means clustering and hierarchical clustering.

4- Dimension Reduction Algorithms

Dimension reduction algorithms are unsupervised learning techniques like clustering algorithms. Sometimes, you may have too many features. Working with so many features both slow down the training of the algorithm and makes it difficult to find a good solution. You can reduce the number of features with size reduction algorithms. You can also use it for tasks such as data visualizing, removing noise, and finding important features. The most commonly used size reduction algorithms are principal component analysis (PCA) and linear discriminant analysis (LDA).

5- Bayesian Algorithms

Bayesian algorithms can be used for both classification and regression problems. Its simplicity, computational ease, good classification performance, and handling of tens of thousands of features are the advantages of these algorithms. There are three most commonly used bayesian algorithms. These are naive bayes, bernoulli naive bayes and multinomial naive bayes. Naive bayes is used for continuous numerical problems, bernoulli naive bayes is used for binary classification problems, and multinomial naive Bayes is used for multi-category classification problems. Bernoulli and multinomial naive bayes algorithms are generally used to classify text data.

6- Association Rule Learning Algorithms

Association rule algorithms are the algorithms that best explain the relationship between the variables in the dataset. For example, it is the rule of association that customers who buy milk and eggs should buy bread. Association rules are often used for recommending products. These algorithms generate associated rules in the IF-THEN format. For example, if a person buys a car, then they can also take out car insurance. The most used relational rule learning algorithms are the apriori algorithm.

7- Artificial Neural Network Algorithms

Nature is always an inspiration for people. For example, Airplanes were invented by looking at birds. Artificial neural networks have also been developed with inspiration from the human brain. Artificial neural networks can be used for both classification and regression problems. The most used artificial neural network algorithms are perceptron, multilayer perceptrons, back-propagation, and stochastic gradient descent.

8- Deep Learning Algorithms

Deep learning has developed very rapidly in the last 10 years. With deep learning algorithms, many problems that could not be solved before, such as driverless cars and object recognition, have been solved. Deep learning algorithms were developed by adding multiple hidden layers to artificial neural networks. Deep learning algorithms are used in many hot research areas of today such as image classification, object recognition, natural language processing, assistant assistants, driverless cars. Algorithms such as convolutional neural networks, recurrent neural networks, and long short-term memory are the most used basic algorithms.

Machine learning algorithms are a huge part of machine learning. In this post, I mentioned the algorithms that data scientists and machine learning engineers should know. That’s it. Thanks for reading. I hope you enjoy it.

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