Machine learning (ML) is a subset of the artificial algorithm. It is the scientific study comprised of algorithms and statistical models that the computer system uses to perform a specific task without any help of explicit instructions. Instead, it replies to data patterns and inference.

It can be defined as the algorithms that parse data sets and then learn from them to apply what has been learned to make informed decisions. In the case of Machine learning, the computer program learns from experience by performing some tasks and sees how those tasks’ performance improves with the experience.

It is the state of the art field of AI that is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music services to data security services.

Related post – 5 Types of Artificial Intelligence Unveiled

What are Machine Learning Algorithms?

 Machine learning algorithms are programs that learn from data without human interference. Here learning tasks means learning the function that drafts the input to the output, and instance-based learning, which means learning the hidden structure in unlabeled data. Machine Learning algorithm is an evolution of the regular algorithm. It enables your programs to be “smarter,” as they allow them to learn from the data provided automatically. We can divide the algorithms into two phases:

• Training Phase
• Testing phase

Machine learning algorithms are based upon data. To solve a specific problem, any machine learning algorithm builds an intuition based on training data. Once the algorithm passes the learning phase, the same knowledge is used to solve similar problems using different datasets.

There are various ways to define the types of machine learning algorithms. However, depending on their purpose, they are commonly divided into four categories:

Supervised learning – The developers supervise this type of algorithm during operation. To perform the task, a developer labels the training data while implying the strict rules for the algorithm to follow. Examples of supervised algorithms are

i.) Regression, 

 ii.) Decision Tree, 

 iii.) Random Forest

 iv.) KNN

 v.) Logistic Regression, etc

Unsupervised Learning – For this category of an algorithm, developers do not directly control the algorithm. Moreover, for this type of algorithms, you remain unknown regarding the desired results. Instead, they are defined by the algorithm. Examples of such algorithms are –

i.) Apriori algorithm and

 ii.) K-means.

Semi-supervised Learning- This type of algorithm is a combination of both supervised and unsupervised algorithms. So, you don’t need to label all training data or imply all rules while initializing the algorithm.

Reinforcement Learning – This algorithm is centered on the exploration technique. Here the machine replicates the process that it performs once. Based on the previous outcome, it performs the next action. Example of this algorithm is –

i.) Markov Decision Process

Each of these categories has a specific purpose – 

• Supervised learning helps to scale the scope of training data and make predictions of new or future data based on that. 
• Unsupervised algorithms organize and filter data to make sense of it.

Each of the categories, as mentioned above, comprises various algorithms and performs certain tasks. This blog has covered 5 basic algorithms that every data scientist must know to cover machine learning basics.

#1: Regression

Regression algorithms are supervised algorithms that are useful to find possible relationships among different variables. It measures how much independent variables affect the dependent one.

You can think of regression analysis as an equation. For example, if you have two independent variables, x and z, and another dependent variable is y, we can express it like y = 2x + z. Regression analysis finds how much do x and z affect the value of y.

You can apply the same logic to more advanced and complex problems. To adapt to the various problems, there are many types of regression algorithms. Here are the top 5 are:

1.    Linear Regression: This is the simplest regression technique that follows a linear approach and features the relationship between dependent and independent variables.

2.  Logistic Regression: This type of Regression applies to binary dependent variables. To analyze categorical data, this type of regressing is widely used.

3.  Ridge Regression: When the regression model becomes too complex, ridge regression corrects the model’s coefficients’ size.

4.  Lasso Regression: Lasso (Least Absolute Shrinkage Selector Operator) Regression is used to select and regularize variables.

5.   Polynomial Regression: This type of algorithm is applicable for non-linear data. Using it, the best prediction is not a straight line; it is a curve that tries to fit all data points.

#2: Classification

Depending on a pre-categorized training dataset, classification algorithms in machine learning group items into categories. Classification is considered a supervised learning algorithm.

Using the training data’s categorization, these algorithms calculate the likelihood of a new item falling into one defined category. A well-known example of classification algorithms is filtering incoming emails into spam or not-spam.

There are different types of classification algorithms; the top 4 ones are:

1.    K-nearest neighbor: KNN is an algorithm that finds the k closest data points in some datasets using the training datasets.

2.  Decision trees: It can be compared with a flow chart that can classify each data point into two categories at a time and then each to two more and so on.

3.  Naive Bayes: This algorithm calculates an item’s probability that falls under a specific category using the conditional probability rule.

4.  Support Vector Machine (SVM): In this algorithm, the data is classified based on its degree of polarity, which can go beyond the X/Y prediction.

#3: Ensembling

As the name suggests, Ensembling algorithms are the combination of two or more other machine learning algorithms prediction that produce more accurate results. Result combination can either be done by averaging the results or voting. Voting is often used during classification and averaging during Regression.

There are three basic types of Ensembling algorithms and these are – Bagging, Boosting, and Stacking.

1.    Bagging: Bagging algorithms run in parallel on different training sets, which are all equal in size. After that, these algorithms are tested using the same dataset, and voting is used to determine the overall results.

2.  Boosting: Boosting algorithms run sequentially. Then using weighted voting, the overall results are selected. 

3.  Stacking: Stacking is a combination of two sets of algorithms. It is two levels that are stacked two levels on top of each other. The base level is a combination of algorithms, and the top level is a meta-algorithm based on the base level results.

#4: Clustering

Clustering algorithms are a collection of unsupervised algorithms which are used to group data points. Points within the same cluster are more similar to each other than to points in different clusters.

There are 4 types of clustering algorithms:

1.    Centroid-based Clustering: Depending on initial conditions and outliers, this clustering algorithm organizes the data into clusters. k-means is the most used centroid-based clustering algorithm.

2.  Density-based Clustering: In this clustering type, arbitrary-shaped distributions are created as the algorithm connects high-density areas into clusters.

3.  Distribution-based Clustering: This clustering algorithm assumes the data is composed of probability distributions and then clusters the data into various versions of that distribution.

4.  Hierarchical Clustering: This algorithm creates a tree of hierarchical data clusters. You can vary the number of clusters by cutting the tree at the correct level.

#5: Association

Association algorithms are unsupervised algorithms that help discover the probability of some items occurring together in a specific dataset. The market basket analysis is based on this algorithm. The most used association algorithm is Apriori.

The Apriori algorithm is a mining algorithm that is commonly used in transactional databases. Apriori is used to mine frequent item sets and generate some association rules from those item sets.

Final Thoughts

This article has discussed 5 types of machine learning algorithms that every machine learning beginner should be familiar with. These algorithms are widely used, and they only need to understand their usability rather than how to implement it.

Now you may not need all the algorithms at a time. So, which one to use depends on many factors that include the size, quality, and nature of data; the available computational time; how urgent the task is, what is the purpose of the data, etc.