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Machine learning algorithms are algorithms created using computer programming. These algorithms read input data sets and analyze them. Based on this analysis, these algorithms predict output values. ML algorithms keep these predictions within an acceptable range of values.
Over time, an ML project team feeds new data to ML algorithms. ML algorithms “learn” from these new data sets and optimize their performance.
Machine learning is an important capability within the superset of artificial intelligence (AI), and that’s because of ML algorithms. Organizations often use them in conjunction with other AI capabilities like computer vision and natural language processing (NLP).
A brief note about machine learning algorithms vs deep learning algorithms
You can think of deep learning as a subset of machine learning. Deep learning algorithms are machine learning algorithms. However, they use a structure of algorithms that resembles the human brain.
This structure of algorithms is called the “Artificial Neural Network” (ANN). Designers of artificial neural networks use the concepts of biological neural networks while designing an ANN. As a result, deep learning algorithms have higher learning capabilities than ML algorithms.
The differences between machine learning algorithms and machine learning models
You might hear people using the terms “machine learning algorithms” and “machine learning models” interchangeably. However, they are different.
An ML algorithm is a procedure. You execute it on a data set to create output. Programmers can describe an ML algorithm in terms of mathematics and pseudocode. You can use popular programming languages to code an algorithm, and you can analyze its performance.
An ML model is the output produced by an ML algorithm. It contains the details of the “learning” of the algorithm.
The nature of an ML algorithm determines the kind of details contained in a model, e.g.:
- An ML model might contain the rules and values that the algorithm “learned”.
- An ML model might include data structures required to create predictions.
- It might contain the procedure to make predictions from input variables.
- In some cases, an ML model might even contain the entire training data set.
The differences between statistical learning models and machine learning models
You might find people using the terms “statistical learning models” and “machine learning models” interchangeably. They are different though.
A project team first makes a set of assumptions before creating a statistical learning model. The team creates a hypothesis, and it validates it with the help of the model. Creating a linear regression model is an example.
A project team doesn’t create a hypothesis before creating a machine learning model. The team runs the relevant algorithms, which make inferences based on the data. There’s no prior assumption to validate.
The different types of machine learning algorithms
The different types of machine learning algorithms are as follows:
1. Supervised learning algorithms
Among different machine learning techniques, supervised learning algorithms use training sets of “labeled” data. The input data set contains known inputs and outputs.
Such data sets have meaningful tags or other identifiers assigned to the data elements. The training process involves the algorithm finding a way to arrive at specific outputs from the input data.
The ML project team feeding this data to the algorithm already knows the desired values of output variables. The algorithm identifies patterns based on the data analysis, and it finds answers. The project team makes corrections.
As this process continues, the ML algorithm can find answers more accurately. Organizations can’t always use machine learning approaches like this since it’s hard to find “labeled” data. You can categorize ML algorithms for classification, regression, and forecasting as “supervised learning” algorithms.
2. Unsupervised learning algorithms
Organizations can find “unlabeled” data sets more commonly. Such data sets don’t include any data point that provides answers to questions. The ML project team doesn’t correct the “learning” of an ML algorithm, therefore, the algorithm needs to analyze data points on its own.
You use unsupervised learning algorithms in such situations. The ML algorithm needs to find patterns in the data set. It must interpret the data on its own, and it needs to find relationships between different data elements.
An unsupervised machine learning algorithm might organize the data in some ways. It might create different groups of data for this.
Machine learning project teams often use unsupervised learning algorithms. This category of algorithms includes clustering and dimensionality reduction algorithms.
A brief note about “association rule learning”
You might have heard about using machine learning for purposes like “market basket analysis”, and you might wonder what kind of ML algorithms should be used for this. We use “association rule learning” for this. It’s a form of unsupervised machine learning.
Association rule learning focuses on the relationship between independent and dependent variables. This type of algorithm tries to find out how the dependent variables are associated with the independent variables.
3. Semi-supervised learning algorithms
Organizations often have access to both “labeled” and “unlabeled” data sets. The following can be a likely scenario:
- You have a small data set with “labeled” data. It wasn’t easy to find that, and this data set costs more.
- You have much larger sets of “unlabeled” data. You could find them easily, and these datasets were cheap.
You can use a “supervised” learning algorithm for the “labeled” data set, and you need to use an unsupervised learning algorithm for the other data sets. The ML algorithms can assign labels to unlabeled data as a part of this “training” process. We call them “semi-supervised learning algorithms”.
4. Reinforcement learning algorithms
Reinforcement learning algorithms are types of machine learning algorithms where the “training” takes place in a “trial-and-error” manner. They work as follows:
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- An ML project team gets a training data set.
- The team establishes a set of actions for the algorithm.
- It sets up a few parameters.
- The project team specifies a list of end-values.
- The ML algorithm works within these rules.
- It examines the training data set and finds different options and patterns.
- The ML algorithm creates a set of outputs.
- A feedback mechanism indicates whether the outputs are correct. There’s a “reward-and-punishment” mechanism too.
- The ML algorithm modifies its approach based on feedback.
Popular machine learning algorithms
The following are popular machine learning algorithms:
1. Naïve Bayes Classifier Algorithm (Supervised Learning Algorithm – Classification)
This popular classifier algorithm is based on the Bayes theorem. ML project teams use this algorithm for classification problems.
The Naïve Bayes classifier algorithm is often used for classifying texts. It’s used in spam filtering and sentiment analysis.
This algorithm classifies all values without dependence on other values. It uses probability. Classification of data points using this algorithm forms the basis of predicting classes and categories.
2. K Means Clustering Algorithm (Unsupervised Learning – Clustering)
Many machine learning or data science project teams use the “K Means Clustering Algorithm” to address clustering problems. This unsupervised learning algorithm groups data in unlabeled training data sets into multiple clusters.
The letter “K” in the name of this algorithm refers to the number of clusters. You need to specify the value of “K” before running this algorithm.
This algorithm finds the value of center points or “centroids”. We call them “K center points” or “K centroids”. Subsequently, it assigns each data point to a cluster around that center point.
3. Support Vector Machine Algorithm (Supervised Learning – Classification Algorithms)
Many ML projects teams use the “Support Vector Machine” (SVM) algorithm for classification. However, some teams use this supervised learning algorithm for regression analysis too.
Project teams use this algorithm to classify data into different categories. They do this by providing training data sets as examples. Project teams divide the training data sets into categories. The SVM algorithm “learns” from these data sets, and it assigns categories to new data points.
4. Linear Regression (Supervised Learning – Regression)
The “Linear Regression” algorithm is one of the popular regression algorithms. Many machine learning project teams use this supervised learning algorithm to predict the values of continuous dependent variables.
They use this algorithm to show relationships between independent and dependent variables. This algorithm produces a linear line indicating the relationship between dependent variables and independent variables. This line is called the “regression line”. Project teams might use different linear equations with this algorithm.
5. Logistic Regression (Supervised Learning – Classification Algorithm)
ML project teams often use the “Logistic Regression” algorithm, which belongs to the category of supervised learning algorithms. This algorithm produces discrete values of dependent variables from independent variables.
Project teams take a set of independent variables. Subsequently, they use the logistic regression algorithm. This is to estimate the discrete values, which predict the values of dependent variables. They might use logistic functions in the process, and they create logistic regression models.
6. Artificial Neural Networks (Reinforcement Learning)
“Artificial Neural Networks” (ANNs) are popular ML algorithms used in deep learning. Developers of ANNs design and develop them by taking ideas from how the human brain operates. They develop ANNs as a set of interconnected algorithms that work together.
ANNs belong to the “reinforcement learning” category of ML algorithms. The training process includes the trial-and-error approach and a feedback loop. ANNs help where the relationship between different input variables is hard for other algorithms to understand.
7. Decision Trees (Supervised Learning – Classification/Regression)
An ML project team might use the “Decision Tree” algorithm for both classification and regression problems. They use this supervised learning algorithm to create flow charts that resemble trees.
The branches of a decision tree represent the different outcomes of a decision. Project teams often use the CART (Classification and Regression Trees) algorithm to build a decision tree.
8. Random Forests (Supervised Learning – Classification/Regression)
The “Random Forest” algorithm can help with both classification and regression problems. ML project teams using this approach might use more than one ML algorithm.
Each of these separate algorithms might produce only weak classifiers. However, the project team then combines them. They can get high-quality outputs in this way.
9. Nearest Neighbors (Supervised Learning Algorithm)
The “Nearest Neighbors” algorithm, also known as the “K-Nearest Neighbors” algorithm uses the supervised learning approach. ML project teams can use it for both classification and regression.
This algorithm first stores all the available data. It doesn’t assign categories to data at this point. The KNN algorithm doesn’t make any assumption about the data at this point either.
When the KNN algorithm gets new data, then it starts analyzing the old data. It assigns categories to old data based on the similarities with the new data.
Other Machine Learning Algorithms
The following are some of the other well-known machine learning algorithms:
1. Dimensionality Reduction Algorithms
Dimensionality reduction algorithms use the unsupervised learning approach. They intend to reduce the number of input variables in the training data sets. There can be many such algorithms, e.g., the “Factor Analysis” algorithm.
2. Gradient Boosting Algorithms
ML project teams use “Gradient Boosting” algorithms to build ML models progressively. They first build one model. Subsequently, they build a model on the errors generated as part of the first model. They continue this process and reduce the errors progressively.
3. Principal Component Analysis
You can use a “Principal Component Analysis” (PCA) algorithm to find principal components in the training data. Your objective is to express the data points with the help of these principal components. ML project teams often use these algorithms for data-cleansing or data-pre-processing.
Supervised algorithms need “labeled” training data, which is hard to find. You can find “unlabeled” training data easier, which suits unsupervised algorithms. Often, you have smaller data sets with “labeled” training data and much “unlabeled” data. They use semi-supervised algorithms here.
The following are a few effective machine learning methods or algorithms for classification: logistic regression algorithm, Naïve Bayes algorithm, K-nearest neighbors algorithm, support vector machines algorithm, and decision tree algorithms.
Data scientists often use several machine learning algorithms. The following machine learning algorithms are often used in data science projects: linear regression, decision trees, k-means clustering, bagging, and support vector machines.