Top 10 Machine Learning Algorithms Examples
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According to Forbes, popular machine learning algorithms may soon replace a wealth of jobs in fields like manufacturing, transportation (hello, self-driving cars), architecture and healthcare.
Besides the huge advantages machine learning can bring to the world, there are huge sums of money to be made too. Here’re a few amazing case studies of companies who hired DevTeam.Space to build their healthcare products:
- Neural Network Library – Machine Learning Application
- Hit Factor – Machine Learning Image Recognition App
- High Speed Vehicle Recognition – Machine Learning Image Recognition Application
What Is Machine Learning (ML)
Let’s cover a few basics first, and let’s start with understanding what ML is. It’s a capability within the overarching umbrella of “Artificial Intelligence” (AI). Check out some pros and cons of this advanced technology in this blog post.
Organizations use ML to create computer systems that can “learn” without explicit programming for “learning”. How do these computer systems learn then? Well, they learn from a very large set of data.
ML utilizes computer algorithms that study and analyze this data, moreover, these algorithms can observe patterns. E.g., ML algorithms can identify examples, experiences, instructions, etc. in the data sets.
These algorithms can then go on to make decisions and predictions. As we feed larger data sets to them, they learn to perform their tasks better. You can read our guide “Machine Learning in future software development” for more insights.
Machine Learning use cases
Machine Learning has a wide range of use in many areas. A few examples are as follows:
- Applications like voice assistants in smartphones;
- Dynamic pricing in the travel industry;
- Email filtering;
- Social media recommendations;
- Personalized marketing;
- Customer support chatbots;
- Fraud detection in banking and financial services institutions;
Read more examples of ML use cases in “Popular Machine Learning applications and use cases in our daily life”.
It’s no wonder then that the market is ML is growing rapidly. A Cision PR Newswire report projects that the global market for ML will reach $96.7 billion in 2025. This report estimates that this market will grow at an impressive CAGR of 43.8% during the 2019-2025 period.
Machine Learning Algorithms explained:
Machine Learning algorithms are used in a variety of applications including:
- Spam filtering
- Image tagging
- Self-driving cars
- Optical Character Recognition
- Anomaly detection
- Association rules and more
Do ML algorithms come in one flavor only? They don’t! Let’s review the various kinds of ML algorithms, which are as follows:
Supervised learning algorithms: These algorithms use known sets of input and out data, i.e., the data is “labeled”. Such algorithms use this labeled data to train computer systems to answer questions.
Unsupervised learning algorithms: These algorithms use “unlabeled data”, i.e., the data sets don’t contain the answers to questions. Computer systems learn to identify hidden patterns and structures in the data sets.
Semi-supervised learning algorithms: These algorithms use both “labeled” and “unlabeled” data sets. In effect, this kind of ML uses both supervised and unsupervised learning algorithms.
Reinforced learning algorithms: ML using these algorithms involve a trial-and-error approach. These algorithms “train” computer systems based on feedback, and computer systems “learn” better over time from the “experience”.
Read more about this in our guide “How to build a Machine Learning filing system to classify books”.
Machine Learning algorithms examples
Some machine learning algorithms are more popular than others. The following are the top 10 machine learning algorithms examples based on popularity and real-world usage.
Artificial Neural Networks
Artificial Neural Networks are named so because they’re based on the structure and functions of real biological neural networks. Information flows through the network and in response, the neural network changes based on the input and output. This machine learning algorithm is used in a number of ways:
- Character recognition (understanding human handwriting and converting it to text)
- Image compression
- Stock market prediction
- Loan applications
One of the most common uses for Artificial Neural Networks is speech recognition. If you’ve ever used Siri, you’ve probably used an ANN. These types of machine learning algorithms get better with more information – they’re constantly growing. Let’s be real: speech recognition has grown leaps and bounds in accuracy over the last five years.
Naïve Bayes Classifier Algorithm
The Naïve Bayes Classifier Algorithm is a classification machine learning algorithm that works off of the popular Bayes Theorem of Probability. It’s one of the most popular learning algorithms that groups similarities, and is usually used in the following ways:
- Disease prediction
- Document classification
The Naïve Bayes Classifier may sound unfamiliar, but you’ve probably encountered it before. The most popular examples are your email spam filter and RSS feeds that filter news into specific categories (Politics, Entertainment, Sports, etc.). This algorithm is particularly useful if you have a moderate or large dataset, if the data has several attributes that can help classify it and if the attributes that describe a certain classification are conditionally independent.
Support Vector Machine Learning Algorithm
Support Vector Machine is one of the many examples of machine learning algorithms catered to classification. This is used for either classification or regression in instances where the set of data teaches the algorithm about specific classes so it can classify newly added data. SVM is constantly growing and evolving.
SVM is commonly used in:
- Stock Market forecasting
- Risk assessment
Most commonly, SVM is used to compare the performance of a stock with other stocks in the same sector. This helps companies make decisions about where they want to invest.
K-Means Clustering Algorithm
The K-Means Clustering Algorithm is one of the most popular machine learning examples. It is commonly used in the following applications:
- Search engines like Yahoo and Bing (to identify relevant results)
- Data libraries
- Google image search
K-Means Clustering is a simple machine learning algorithm used for clustering, meaning it helps group together similar data sets. This could be anything from images and videos to text documents and web pages. For example, you’re searching Wikipedia for the word Apple. This could bring up results for both Apple, the technology company, and apple, the fruit. K-Means Clustering would group together results about the technology company apart from results about the fruit, so you can get meaningful results on the actual topic you want to read about.
K-Nearest Neighbors Algorithm
Like K-Means Clustering, K-Nearest Neighbors is another classification and regression machine learning algorithm. It’s most commonly used in:
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- Pattern recognition (like to predict how cancer may spread)
- Statistical estimation (like to predict if someone may default on a loan)
K-Nearest Neighbors makes predictions by searching through the whole dataset to find the most similar instances (the neighbors) and summarizing the output variable for those instances. Objects are classified by majority votes and assigned the class most common to its neighbors.
Decision Tree Machine Learning Algorithm
Decision Trees are graphical representations that show all possible outcomes of a decision based on certain conditions. It’s typically used for two things – to classify or to predict – and remains one of the best machine learning algorithms for classification. It has been used in the following ways:
- To help banks classify loan applicants and their probability of defaulting payments
- To help Gerber Products decide whether or not to use PVC in their baby products
- Identify at-risk patients and disease trends with Guardian, a tool developed by Rush University Medical Centre
The decision tree algorithm falls into either Classification Trees or Regression Trees. Classification trees are the default and used to split data into different classes based on the response variable. Regression Trees are used when the target variable is continuous or numerical. This is typically used in a predictive nature. Because of the nature of the algorithm, if your data has errors, so will your decision tree. It’s best suited towards extensive, meticulously correct data.
Apriori Machine Learning Algorithm
Apriori algorithm is a data-mining machine learning algorithm that generates association rules for a given set of data. It has been used in everything from a college elective system that helps students choose classes to a database that discovers the social status of diabetic people. Its most popular applications include:
- Google auto-complete
- Amazon shopping recommendations
- Detecting adverse drug reactions
Apriori works by using association rules from a given data set. These rules imply that if A occurs, B also occurs. For example, Wal-Mart actually used the Apriori algorithm to increase sales of beer. Wal-Mart studied their data to find that American males who bought diapers on Friday afternoons also frequently bought beer. They moved the beer next to the diapers, and sales increased.
Apriori is beneficial in more than a couple of ways. It also happens to be one of the easiest machine learning algorithms to implement.
Linear Regression Machine Learning Algorithm
Linear Regression is one of the most interpretable machine learning algorithms. It’s easy to explain to others and requires minimal tuning. This is perhaps why it’s one of the most popular algorithms. It can be used to:
- Estimate Sales
- Assess Risk
Linear regression works by showing a relationship between two variables and how the change of one variable affects the other. This is why it’s so great in risk assessment and business. For example, health insurance brokers often use this algorithm to analyze the number of claims per customer against their age. If insurance companies find that older customers tended to make more claims, they increase rates for older customers. If they found that older customers didn’t have more accidents, they could lower the rates.
Random Forest Machine Learning Algorithm
Random Forests or Random Decision Forests are a machine learning method of classification and regression. You’ve probably seen them used in the following ways:
- To help banks predict high-risk loan applicants
- To predict failure or breakdown of a mechanical part
- To predict if a patient is likely to develop a chronic disease
- To predict the average number of social media shares on a post
Its versatility is what gives this algorithm its popularity. Instead of using a single decision tree, Random Forest uses a multitude of decision trees to come up with a solid classification or prediction. This ensures more accurate classification because each decision tree is given slightly different data. These variables are very effective because they help preserve accuracy when data is missing. It’s also fairly resistant to outliers (majority always rules) and easily implemented in a couple of lines of code.
No, Logistic Regression isn’t for regression problems. It’s actually for classification tasks. The algorithm applies a logistic function to a combination of features that predicts the outcome of a dependent variable. Of course, it wouldn‘t be true to the name if the variable wasn’t based on already predicted variables. It’s split up into three categories:
- Binary Logistic Regression
- Multi-nominal Logistic Regression
- Ordinal Logistic Regression
Binary Logistic Regression is most commonly used when there are two possible outcomes (yes or no; pass or fail). This can help in ways such as predicting if a student is likely to pass or fail a course or predicting if a tumor is cancerous or not. Multi-nominal Logistic Regression has three or more outcomes with no order, and Ordinal Logistic Regression has three or more outcomes with a natural ordering.
Developing a Machine Learning algorithm: What skills do you in your team?
Now that you have clarity about the popular ML algorithms, you are likely thinking about how to develop one for your app. The key question that probably comes to your mind is: “What skills do I need in my team?”.
At the time of writing this guide, Python is the most popular language for ML development. Python is simple, and that contributes greatly to its popularity.
Compared to several other languages, you need a shorter time to develop ML code in Python. This popular language has many libraries that make programming easier. Take the example of “Pybrain”, which is a library for ML coding using Python. Read more about the popularity of Python in “Top 5 best programming languages for Artificial Intelligence field”.
Wondering how to find competent Python developers? We at DevTeam.Space have just the right expertise you need. Read “Julia VS Python: Can this new programming language unseat the king?” to judge our capabilities.
This list of machine learning algorithms is only the very tip of the iceberg. If you’re looking to implement artificial intelligence and machine learning into your business, DevTeamSpace can put you in touch with the developers who can make it happen. Just post a simple request.
Frequently Asked Questions
What is machine learning algorithms?
These are formulas that allow computer programs to identify trends or patterns in data pools and to cross reference the results with past results to improve future results.
What are regression algorithms in machine learning?
Regression algorithms are part of the Supervised Machine Learning algorithms family. Regression algorithms are used to predict the output values from pools of data.
What is the difference between AI and ML?
AI or artificial intelligence is currently a stage of computer intelligent that has not yet been reached. Current systems are ML or machine learning systems which are able to cope with a narrow set of interpretations. AI will arrive when computers are able to learn new tasks and skills independently of humans.