Machine learning algorithms: A tour of ML algorithms & applications

Updated May 31, 2022

The revolutionary potential for machine learning to shift growth strategies in the business world is tough to overstate. As new projects have gained notoriety through their use of this emerging technology, its many strengths and uses have become self-evident.

For more information on the uses of AI in business development, download our white paper, How AI Improves the Customer Experience.

Thanks to machine learning, more information than ever before can be efficiently processed and transformed from a mess of uninterpreted data points to intuitive reports and actionable insights that can drive decision-making, improve customer experiences and much more.

What is Machine Learning?

At its core, machine learning centers on the ability a system has to improve its performance of a given task over time without manually being adjusted to do so.

Generally, machine learning helps a system to recognize patterns, predict outcomes and plan, intuitively.

Machine learning as a growing body of techniques owes much of its development to the efforts of researchers interested in modeling the human mind. In so doing, their attempts – computational models designed to test theoretical hunches – bore fruit in granting machines the capacity for selective reasoning.

Although machine learning remains limited in comparison to organic, human learning capabilities, it has proven especially useful for automating the interpretation of large and diverse stores of data.

Machine learning algorithms can be sorted into the following categories:

Reinforcement Learning

These types of algorithms learn to improve their effectiveness through trial and error.

Supervised Learning

This category includes algorithms that improve in effectiveness by learning what function best maps input variables to an output variable.

Unsupervised Learning

Algorithms in this category operate similarly to those in that of supervised learning, but they lack a predefined output variable.

Ensemble Learning

This group of algorithms makes use of multiple learners to validate results more thoroughly by voting on them either in parallel or sequentially.

Read on to learn more about machine learning algorithms and their current uses in a variety of industries.

Machine Learning Algorithms

1. Classification and Regression Trees follow a map of boolean (yes/no) conditions to predict outcomes.

“Classification and Regression Trees (CART) is an implementation of Decision Trees, among others such as ID3, C4.5.

“The non-terminal nodes are the root node and the internal node. The terminal nodes are the leaf nodes. Each non-terminal node represents a single input variable (x) and a splitting point on that variable; the leaf nodes represent the output variable (y). The model is used as follows to make predictions: walk the splits of the tree to arrive at a leaf node and output the value present at the leaf node.” – Reena Shaw, Top 10 Machine Learning Algorithms for Beginners, KDnuggets; Twitter: @kdnuggets

2. The Apriori algorithm is best suited for sorting data.

“The Apriori algorithm is a categorization algorithm. Some algorithms are used to create binary appraisals of information or find a regression relationship. Others are used to predict trends and patterns that are originally identified. Apriori is a basic machine learning algorithm which is used to sort information into categories. Sorting information can be incredibly helpful with any data management process. It ensures that data users are appraised of new information and can figure out the data that they are working with.” – John Wingate, Apriori Algorithm, Engineering Big Data; Twitter: @EngBigData

3. Boosting helps to prevent bias from individual learners coloring results.

“Sequential ensemble, popularly known as boosting, here the weak learners are sequentially produced during the training phase. The performance of the model is improved by assigning a higher weightage to the previous, incorrectly classified samples. An example of boosting is the AdaBoost algorithm.” – Zulaikha Lateef, A Beginner’s Guide to Boosting Machine Learning Algorithms, Edureka; Twitter: @edurekaIN

4. The K-Nearest Neighbors (KNN) algorithm uses distance between data points to help sort them.

“The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other.” – Onel Harrison, Machine Learning Basics with the K-Nearest Neighbors Algorithm, Towards Data Science; Twitter: @onelharrison

5. The K-means algorithm uses central data points in data sets to compile clusters of data.

“K-Means clustering is an unsupervised learning algorithm that, as the name hints, finds a fixed number (k) of clusters in a set of data. A cluster is a group of data points that are grouped together due to similarities in their features. When using a K-Means algorithm, a cluster is defined by a centroid, which is a point (either imaginary or real) at the center of a cluster. Every point in a data set is part of the cluster whose centroid is most closely located. To put it simply, K-Means finds k number of centroids, and then assigns all data points to the closest cluster, with the aim of keeping the centroids small.” – Machine Learning Algorithms Explained – K-Means Clustering, EasySol.net

6. Random forests make bagging techniques more effective.

“A problem with decision trees like CART is that they are greedy. They choose which variable to split on using a greedy algorithm that minimizes error. As such, even with Bagging, the decision trees can have a lot of structural similarities and in turn have high correlation in their predictions.

“Combining predictions from multiple models in ensembles works better if the predictions from the sub-models are uncorrelated or at best weakly correlated.

“Random forest changes the algorithm for the way that the sub-trees are learned so that the resulting predictions from all of the subtrees have less correlation.

“It is a simple tweak. In CART, when selecting a split point, the learning algorithm is allowed to look through all variables and all variable values in order to select the most optimal split-point. The random forest algorithm changes this procedure so that the learning algorithm is limited to a random sample of features of which to search.” – Jason Brownlee, Bagging and Random Forest Ensemble Algorithms for Machine Learning, Machine Learning Mastery; Twitter: @TeachTheMachine

7. The Naïve Bayes classifier discerns between data characteristics without connecting them to one another.

“In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Yes, it is really Naïve! […] The algorithm first creates a frequency table (similar to prior probability) of all classes and then creates a likelihood table. Then, finally, it calculates the posterior probability.” – Anand Venkataraman, Naïve Bayes for Machine Learning, FloydHub; Twitter: @FloydHub_

8. Linear Regression helps in predicting correlated values.

“Linear regression is one of the most powerful and yet very simple machine learning algorithms. Linear regression is used for cases where the relationship between the dependent and one or more of the independent variables is supposed to be linearly correlated.” – RP, Python Machine Learning Linear Regression with Scikit- learn, Datafai.com

9. Principal Component Analysis (PCA) identifies connecting characteristics among variables.

“Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables.” – ML | Principal Component Analysis(PCA), GeeksforGeeks; Twitter: @geeksforgeeks

10. Logistic Regression helps determine the probability of data representing an expected value.

“You can think of logistic regression as an on-off switch. It can stand alone, or some version of it may be used as a mathematical component to form switches, or gates, that relay or block the flow of information. […]Logistic regression is widely used in statistics, and it was originally applied in ecology to the study of populations, whose growth tends to plateau as they exhaust the resources at their disposal.” – Chris Nicholson, A Beginner’s Guide to Logistic Regression For Machine Learning, PathMind; Twitter: @chrisvnicholson

Machine Learning Applications

11. Speech analytics analyzes the tone, sentiment, vocabulary, and other factors in callers’ voices to route callers to the right call center agents.

“Speech analytics is another newer technology increasingly utilized in the call center. Also known as voice analytics, this technology was first used in enterprises such as call centers in the early 2000s for commercial purposes. It goes beyond recognition, interpreting not just the words a caller speaks but also the manner in which those words are spoken. Speech analytics detects factors such as tone, sentiment, vocabulary, silent pauses, and even the caller’s age, analyzing these factors to route callers to the ideal agent based on agents’ success rates, specialized knowledge and strengths, as well as the customer’s personality and other behavioral characteristics.

“In addition to analytics, the modern use of AI is closely interwoven with concepts such as machine learning (ML), data mining, big data, and automation. Combining AI with technologies such as predictive analytics can result in a more powerful, more scalable, and more efficient application of data.” – Robert Stanley, A Comprehensive History of AI in the Call Center: From ACDs to Predictive Analytics and Beyond, CallMiner; Twitter: @CallMiner

12. Machine learning powers advancements in fraud prevention.

“Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.” – Bernard Marr, The Top 10 AI And Machine Learning Use Cases Everyone Should Know About, Forbes; Twitter: @bernardmarr

13. Video surveillance is supported by machine learning.

“The video surveillance systems nowadays are powered by AI that makes it possible to detect crime before they happen. They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps.” – 9 Applications of Machine Learning from Day-to-Day Life, Daffodil Software; Twitter: @daffodilsw

14. Image recognition benefits from machine learning techniques.

“Image recognition is one of the most significant machine learning and artificial intelligence examples. Basically, it is an approach for identifying and detecting a feature or an object in the digital image. Moreover, this technique can be used for further analysis, such as pattern recognition, face detection, face recognition, optical character recognition, and many more.” – Mehedi Hasan, Top 20 Best AI Examples and Machine Learning Applications, UbuntuPit; Twitter: @Ubuntu_PIT

15. Machine learning makes speech recognition possible.

“In speech recognition, a software application recognizes spoken words. The measurements in this application might be a set of numbers that represent the speech signal. We can segment the signal into portions that contain distinct words or phonemes. In each segment, we can represent the speech signal by the intensities or energy in different time-frequency bands.” – Sheetal Sharma, Top 9 Machine Learning Applications in Real World, Data Science Central; Twitter: @DataScienceCtrl

16. Machine learning helps predict customer lifetime value.

“Fashion retailer Asos uses machine learning to determine Customer Lifetime Value (CLTV). This metric estimates the net profit a business receives from a specific customer over time. In Asos’ case, CLTV shows which customers are likely to continue buying products from Asos. Once this is determined, Asos can prioritize high-CLTV customers and convince them to spend more the next time around. Because retailers can end up losing money on low-CLTV (with things like free shipping or ignored marketing promos), this model ensures that Asos is turning a profit.” – Gordon Gottsegen, 15 examples of machine learning making established industries smarter, Built In; Twitter: @builtin

17. ML personalizes entertainment experiences.

“Machine learning has tremendous applications in digital media, social media and entertainment. Personalized recommendation (i.e., YouTube video recommendation), user behavior analysis, spam filtering, social media analysis, and monitoring are some of the most important applications of machine learning.” – Application of machine learning, EDUCBA

18. Email filtering is strengthened with ML.

“Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive an important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. […] Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier are used for email spam filtering and malware detection.” – Applications of Machine Learning, Javatpoint; Twitter: @pagejavatpoint

19. ML powers autonomous trading in finance.

“Machine learning is integral to the advantages of algorithmic programs. It allows traders to automate certain processes ensuring a competitive advantage. The system also makes it possible to operate in multiple markets, increasing trading opportunities.

“In addition, the algorithms are able to learn and adapt to real-time changes, which is another competitive advantage for those institutions that adopt machine learning in finance.” – KC Cheung, 10 Applications of Machine Learning in Finance, Algorithm-X Lab; Twitter: @AlgorithmXLab

20. Machine learning enhances data acquisition efforts.

“Google has widely implemented machine learning technologies in its products and services to benefit from the massive information it can obtain by doing so. […] The Cloud Vision API provides developers with powerful machine learning models for processing image content. Additionally, the Cloud Machine Learning Engine allows technical professionals to train their machine learning models at scale.” – Anna Bryk, Machine Learning – Existing Applications, Apriorit; Twitter: @apriorit

21. ML streamlines healthcare tasks.

“Another application area of machine learning is in medical diagnosis. In a healthcare system, machine learning combines the doctor’s knowledge and makes the treatment more efficient and reliable. ML algorithm is used for diagnostic, personalized medicine, and other areas where time matters.” – Daria Dubrova, Machine Learning for Mobile Apps. How to Use?, The App Solutions; Twitter: @TheAPPSolutions

22. Machine learning powers automatic translation.

“While a simple concept, machine learning can also be used to instantly translate text into another language. Not only this, but it can do the same thing with text on images! In the case of text, the algorithm can learn about how words fit together and translate more accurately.

“In the case of images, the neural network identifies letters in the image, pulls them into text, and then does the translation before putting them back into the picture.” – Mariane Davids, 5 Applications of Machine Learning, Robotiq; Twitter: @Robotiq_Inc

23. Dynamic pricing strategies benefit immensely from machine learning.

“Dynamic pricing, also known as demand pricing, is the practice of flexibly pricing items based on factors like the level of interest of the target customer, demand at the time of purchase, or whether the customer has engaged with a marketing campaign. This requires a lot of data about how different customers’ willingness to pay for a good or service changes across a variety of situations, but companies like airlines and ride-share services have successfully implemented dynamic price optimization strategies to maximize revenue.” – Nikki Castle, 6 Common Machine Learning Applications for Business, Oracle; Twitter: @Oracle

24. ML breaks the language barrier in cybersecurity.

“Natural language processing, also known as NLP, poses huge benefits for cybersecurity because it enables machines to gather and make sense of data irrespective of language, format, and punctuation. Powerful NLP engines are even able to understand common slang and jargon across all languages, something a team of analysts could never aspire to.” – Machine Learning: Practical Applications for Cybersecurity, Recorded Future; Twitter: @RecordedFuture

25. ML helps regulate the flow of traffic.

“You know how much we all hate sitting in our vehicles, waiting for the lights to turn green, especially when there aren’t any vehicles coming in from the opposite side, but the traffic lights aren’t that smart, or are they? Well, Artificial Intelligence and Machine Learning algorithms seem to be taking over the streets of many countries and they’re efficiently able to predict, monitor, and manage the traffic.” – Scarlett Rose, Machine Learning Applications Across Different Industries, Hackernoon; Twitter: @hackernoon

How has your business leveraged machine learning for further development?