Machine Learning (ML) is a technological innovation that continues to prove its worth in many sectors.
Machine learning is related to artificial intelligence and deep learning. Since we live in a constantly progressing technological era, it’s now possible to predict what comes next and know how to change our approach using ML.
Thus, you are not limited to manual ways; almost every task nowadays is automated. There are different machine learning algorithms designed for different work. These algorithms can solve complex problems and save hours of business time.
Examples of this could be playing chess, filling out data, performing surgeries, choosing the best option from the shopping list, and many more.
I’ll explain machine learning algorithms and models in detail in this article.
Here we go!
What is Machine Learning?
Machine learning is a skill or technology where a machine (such as a computer) needs to build the ability to learn and adapt by using statistical models and algorithms without being highly programmed.
As a result of this, machines behave similarly to humans. It is a type of Artificial Intelligence that allows software applications to become more accurate at predictions and performing different tasks by leveraging data and improving itself.
Since computing technologies are growing rapidly, today’s machine learning is not the same as the past machine learning. Machine learning proves its existence from pattern recognition to the theory of learning to perform certain tasks.
With machine learning, computers learn from previous computations to produce repeatable, reliable decisions and results. In other words, machine learning is a science that has gained fresh momentum.
Although many algorithms have been used for a long time, the ability to apply complex calculations automatically to big data, faster and faster, over and over, is a recent development.
Some publicized examples are as follows:
- Online recommendation discounts and offers, such as from Netflix and Amazon
- Self-driving and heavily hyped Google car
- Detection of fraud and suggesting some ways to skip those problems
And many more.
Why do you need Machine Learning?
Machine learning is an important concept that every business owner implements in their software applications to know their customer behavior, business operational patterns, and more. It supports the development of the latest products.
Many leading companies, like Google, Uber, Instagram, Amazon, etc., make machine learning their central part of operations. However, industries working on a large amount of data know the importance of machine learning models.
Organizations are able to work efficiently with this technology. Industries like financial services, government, health care, retail, transportation, and oil-gas use machine learning models to deliver more valuable customer results.
Who is using Machine Learning?
Machine learning nowadays is used in numerous applications. The most well-known example is the recommendation engine on Instagram, Facebook, Twitter, etc.
Facebook is using machine learning to personalize members’ experiences on their news feeds. If a user frequently stops to check the same category of posts, the recommendation engine starts to show more of the same category posts.
Behind the screen, the recommendation engine attempts to study the members’ online behavior through their patterns. The news feed adjusts automatically when the user changes its action.
Related to recommendation engines, many enterprises use the same concept to run their critical business procedures. They are:
- Customer Relationship Management (CRM) software: It uses machine learning models to analyze visitors’ emails and prompt the sales team to respond immediately to the most important messages first.
- Business Intelligence (BI): Analytics and BI vendors use the technology to identify essential data points, patterns, and anomalies.
- Human Resource Information Systems (HRIS): It uses machine learning models in its software to filter through its applications and recognize the best candidates for the required position.
- Self-driving cars: Machine learning algorithms make it possible for car manufacturing companies to identify the object or sense the driver’s behavior to alert immediately to prevent accidents.
- Virtual assistants: Virtual assistants are smart assistants that combine supervised and unsupervised models to interpret speech and supply context.
What are Machine Learning Models?
An ML model is a computer software or application trained to judge and recognize some patterns. You can train the model with the help of data and supply it with the algorithm so that it learns from that data.
For example, you want to make an application that recognizes emotions based on the user’s facial expressions. Here, you need to feed the model with different images of faces labelled with different emotions and train your model well. Now, you can use the same model in your application to easily determine the user’s mood.
In simple terms, a machine learning model is a simplified process representation. This is the easiest way to determine something or recommend something to a consumer. Everything in the model works as an approximation.
For example, when we draw a globe or manufacture it, we give it the shape of a sphere. But the actual globe is not spherical as we know. Here, we assume the shape to build something. The ML models work similarly.
Let’s go ahead with the different machine-learning models and algorithms.
Types of Machine Learning Models
All the machine learning models are categorized as supervised, unsupervised, and reinforcement learning. Supervised and unsupervised learning is further classified as different terms. Let’s discuss each one of them in detail.
Supervised learning is a straightforward machine learning model that involves learning a basic function. This function maps an input to the output. For example, if you have a dataset consisting of two variables, age as input and height as output.
With a supervised learning model, you can easily predict the height of a person based on age of that person. To understand this learning model, you must go through the sub-categories.
Classification is a widely used predictive modelling task in the field of machine learning where a label is predicted for a given input data. It necessitates the training data set with a wide range of instances of inputs and outputs from which the model learns.
The training data set is used to find the minimum way to map input data samples to the specified class labels. Finally, the training data set represents the issue that contains a large number of output samples.
It is used for spam filtering, document search, handwritten character recognition, fraud detection, language identification, and sentiment analysis. The output is discrete in this case.
In this model, the output is always continuous. Regression analysis is essentially a statistical approach that model a connection between one or more variables that are independent and a target or dependent variable.
Regression allows seeing how the number of the dependent variable changes in relation to the independent variable while the other independent variables are constant. It is used to predict salary, age, temperature, price, and other real data.
Regression analysis is a “best guess” method that generates a forecast from the set of data. In simple words, fitting various points of data into a graph in order to get the most precise value.
Example: Predicting the price of a flight ticket is a common regression job.
Unsupervised learning is essentially used to draw inferences as well as find patterns from the input data without any references to the labelled outcomes. This technique is used to discover hidden data groupings and patterns without the need for human intervention.
It can discover differences and similarities in information, making this technique ideal for customer segmentation, exploratory data analysis, pattern and image recognition, and cross-selling strategies.
Unsupervised learning is also used to reduce a model’s finite number of features using the dimensionality reduction process that includes two approaches: singular value decomposition and principal component analysis.
Clustering is an unsupervised learning model that includes the grouping of the data points. It is used frequently for fraud detection, document classification, and customer segmentation.
The most common clustering or grouping algorithms include hierarchical clustering, density-based clustering, mean shift clustering, and k-means clustering. Every algorithm is used differently to find clusters, but the aim is the same in every case.
#2. Dimensionality Reduction
It is a method of reducing various random variables that are under consideration to obtain a set of principal variables. In other words, the process of decreasing the dimension of the feature set is called dimensionality reduction. The popular algorithm of this model is called Principal Component Analysis.
The curse of this refers to the fact of adding more input to predictive modeling activities, which makes it even more difficult to model. It is generally used for data visualization.
Reinforcement learning is a learning paradigm where an agent learns to interact with the environment and for the correct set of actions, it occasionally gets a reward.
The reinforcement learning model learns as it moves forward with the trial and error method. The sequence of successful results forced the model to develop the best recommendation for a given problem. This is often used in gaming, navigation, robotics, and more.
Types of Machine Learning Algorithms
#1. Linear Regression
Here, the idea is to find a line that fits the data you need in the best way possible. There are extensions in the linear regression model that includes multiple linear regression and polynomial regression. This means finding the best plane that fits the data and the best curve that fits the data, respectively.
#2. Logistic Regression
Logistic regression is very similar to the linear regression algorithm but is essentially used to get a finite number of outcomes, let’s say two. Logistic regression is used over linear regression while modelling the probability of outcomes.
Here, a logistic equation is built in a brilliant way so that the output variable will be between 0 and 1.
#3. Decision Tree
The decision tree model is widely used in strategic planning, machine learning, and operations research. It consists of nodes. If you have more nodes, you will get more accurate results. The last node of the decision tree consists of data that help make decisions faster.
Thus, the last nodes are also referred to as the leaves of the trees. Decision trees are easy and intuitive to build, but they fall short in terms of accuracy.
#4. Random Forest
It is an ensemble learning technique. In simple terms, it is built off of decision trees. The random forests model involves multiple decision trees by using bootstrapped datasets of the true data. It randomly selects the subset of the variables at every step of the tree.
The random forest model selects the mode of prediction of every decision tree. Hence, relying on the “majority wins” model reduces the risk of error.
For example, if you create an individual decision tree and the model predicts 0 at the end, you will not have anything. But if you create 4 decision trees at a time, you might get value 1. This is the power of the random forest learning model.
#5. Support Vector Machine
A Support Vector Machine (SVM) is a supervised machine learning algorithm that is complicated but intuitive when we talk about the most fundamental level.
For example, if there are two types of data or classes, the SVM algorithm will find a boundary or a hyperplane between that classes of data and maximizes the margin between the two. There are many planes or boundaries that separate two classes, but one plane can maximize the distance or margin between the classes.
#6. Principal Component Analysis (PCA)
Principal component analysis means projecting higher dimensional information, such as 3 dimensions, to a smaller space, such as 2 dimensions. This results in a minimal dimension of data. This way, you can keep the original values in the model without hampering the position but reducing the dimensions.
In simple words, it is a dimension-reduction model which is especially used to bring multiple variables present in the data set down to the least variables. It can be done by putting those variables together whose measurement scale is the same and has higher correlations than others.
The primary goal of this algorithm is to show you the new groups of variables and give you enough access to get your work done.
For example, PCA helps interpret surveys that include many questions or variables, such as surveys on well-being, study culture, or behavior. You can see minimal variables of this with the PCA model.
#7. Naive Bayes
The Naive Bayes algorithm is used in data science and is a popular model used in many industries. The idea is taken from the Bayes Theorem that explains the probability equation like “what is the probability of Q (output variable) given P.
It is a mathematical explanation that is used in today’s technological era.
Apart from these, some models mentioned in the regression part, including decision tree, neural network, and random forest, also come under the classification model. The only difference between the terms is that the output is discrete instead of continuous.
#8. Neural Network
A neural network is again the most used model in industries. It is essentially a network of various mathematical equations. First, it takes one or more variables as input and goes through the network of equations. In the end, it gives you results in one or more output variables.
In other words, a neural network takes a vector of inputs and returns the vector of outputs. It is similar to matrices in mathematics. It has hidden layers in the middle of the input and output layers representing both linear and activation functions.
#9. K-Nearest Neighbours (KNN) Algorithm
The KNN algorithm is used for both classification and regression problems. It is widely used in the data science industry to solve classification problems. Moreover, it stores all the available cases and classifies coming cases by taking the votes of its k neighbors.
The distance function performs the measurement. For example, if you want data about a person, you need to talk to the nearest people to that person, such as friends, colleagues, etc. In a similar way, the KNN algorithm works.
You need to consider three things before selecting the KNN algorithm.
- Data needs to be pre-processed.
- Variables need to be normalized, or higher variables can bias the model.
- The KNN is computationally expensive.
#10. K-Means Clustering
It comes under an unsupervised machine learning model that solves the clustering tasks. Here data sets are classified and categorized into several clusters (let’s say K) so that all the points within a cluster are heterogenous and homogenous from the data.
K-Means forms clusters like this:
- The K-Means picks the K number of data points, called centroids, for every cluster.
- Every data point forms a cluster with the closest cluster (centroids), i.e., K clusters.
- This creates new centroids.
- The closest distance for each point is then determined. This process repeats until the centroids do not change.
Machine learning models and algorithms are very decisive for critical processes. These algorithms make our day-to-day life easy and simple. This way, it becomes easier to bring out the most gigantic processes in seconds.
Thus, ML is a powerful tool that many industries nowadays use, and its demand is growing continuously. And the day is not far when we can get even more precise answers to our complex problems.