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Machine learning (ML) is ubiquitous these days. It supports most AI-related programs, from interactive chatbots to predictive text to the algorithm behind social media feeds to self-driving cars.
When companies deploy artificial intelligence programs, they often use machine learning techniques to train the computers to learn without explicitly being programmed. In that regard, ML is a subfield of artificial intelligence, although these two terms have been used interchangeably.
More and more companies are now using ML to unlock new value or boost efficiency. A 2020 Deloitte survey found that 67% of companies are using it, and 97% had planned to use it by the end of 2022.
So how does machine learning actually work?
In this blog article, we will dive deep into the tech behind machine learning and understand its social and business implications.
Machine learning is a subset of artificial intelligence that aims to equip machines with the capabilities of imitating human intelligence and behaviour. That way, they can perform complex tasks similar to how humans solve problems. The goal here is to create computers that can exhibit intelligence like humans by recognising a visual scene, understanding a text written in natural language, or performing a human-like action in the physical world.
Machine learning is an important part of data science. Through statistical methods, algorithms can make predictions to uncover patterns and key insights in data mining projects. Any task that can be completed with a data-defined pattern can be automated with ML. Companies can then transform previously tedious processes, such as customer segmentation, bookkeeping, and sorting resumes, to name a few.
Moreover, machine learning algorithms use historical data as input to predict new output values. ML is incredibly important to businesses because it gives them a view of trends in customer behaviour, business operation patterns, and new developments. That means ML has become a competitive differentiator for many businesses these days.
Although these terms are often used interchangeably, there are major differences. And the best way to think about artificial intelligence, machine learning, deep learning, and neural networks is to visualise Matryoshka dolls, i.e. Russian nesting dolls.
Artificial intelligence encompasses all the other terms, and each is essentially a component of the prior term. That means ML is a subfield of artificial intelligence, while deep learning is a subfield of ML.
At the highest level, AI is defined as a way of leveraging computers or machines to mimic the problem-solving and decision-making capabilities of the human mind. ML is a subset of artificial intelligence that’s more focussed on using various self-learning algorithms that derive knowledge from data to predict outcomes.
On the other hand, deep learning is a further subset of ML often thought of as “scalable machine learning”, meaning that it automates a lot of features and eliminates some of the human intervention. Neural networks are then stripping away individual elements of deep learning and focusing on the “depth” of input and output layers.
Let’s understand a bit more about each term.
Artificial intelligence is a process of programming a computer to make decisions for itself. This can be done through several different methods, such as rule-based systems, decision trees, genetic algorithms, artificial neural networks, and fuzzy logic systems.
Machine learning is a subset of AI that deals with creating algorithms that can learn from and make predictions on data. This is done through many different methods, such as supervised, unsupervised, reinforcement, and semi-supervised learning.
Deep learning is a subset of ML that deals with creating algorithms that can learn from data structured in layers. This is done through many different methods, such as deep neural networks, convolutional neural networks, and recurrent neural networks.
Neural networks are a subset of deep learning that deals with mimicking the human brain through a set of algorithms. This is done through four major components: inputs, weights, a bias or threshold, and an output. While it’s already implied, the “deep” in deep learning refers to the depths of layers in neural networks.
Machine learning uses four main techniques, viz supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
In supervised ML, labelled data sets train algorithms to accurately classify data or predict outcomes. “Labelled” means that the rules in the data set are labelled, tagged, or classified in some interesting way that tells us something about that data. As the data is fed into the system, supervised learning adjusts its weight until it is fitted properly.
The data sets are designed to train or “supervise” algorithms to classify data or predict outcomes. Using labelled input and output data, this model can measure its own accuracy and learn over time.
Supervised learning can be further classified into two categories: classification and regression.
Classification recognises and groups ideas and objects into predefined categories. Simply put, it uses an algorithm to accurately assign test data into specific categories. It’s similar to separating apples from oranges.
Here’s an example from the real world: customer retention. If you are in the business of managing customers, one of the goals is typically identifying and minimising customer churn, i.e. customers who no longer buy a particular product or service. In this case, with the historical data of the customers, supervised learning can help us build a classification model with labelled data sets. This will help businesses identify customers who are about to churn and allow them to take action to retain those customers.
It is another type of supervised learning that uses algorithms to understand the relationship between independent and dependent variables. The model helps predict numerical values based on different data points.
In other words, regression analysis happens when software engineers build an equation using various input variables with their specific weights determined by the overall value of their impact on the outcome. This is then used to generate and estimate the output values.
Here’s an example from the real world: determining the fare of airline tickets by using regression analysis. Airline companies use various input factors, such as days before departure, the day of the week, and the origin and destination, to predict an accurate dollar value for how much they should be charging for a specific flight.
Unsupervised machine learning uses algorithms to analyse and cluster unlabelled data sets. These algorithms discover hidden patterns within the data without needing any human intervention.
Unsupervised models are used for three main tasks: clustering, association, and dimensionality reduction.
Clustering is a data mining technique that groups unlabelled data based on its similarities and differences. It assigns similar data points into groups. This technique is helpful in market segmentation, customer grouping, and image compression, among others.
Here’s an example from the real world: organisations doing customer segmentation. Through customer segmentation, businesses try to do effective marketing to really understand who their customers are so that they can connect with them in the most relevant way. It is often unclear how customers are similar to or different from each other. Therefore, clustering algorithms can help companies group customers based on their various characteristics, such as purchasing habits, social media activity, or geographic location.
Association uses unsupervised learning to find relationships between variables in a given data set. It is frequently used for market analysis and recommendation. For instance, the ads you see on social media based on “you may like this” are based on the association technique. Or “customers who bought this item also bought” suggestions you see on an eCommerce website.
Association rules help companies better understand their consumers’ consumption habits and enable them to develop cross-selling strategies and recommendations.
Here’s a use case from the real world: in the music industry, apriori algorithms following the association rules are used in predicting the likelihood of customers buying a product based on their previous purchases. For example, if a customer subscribes to a Bob Dylan album on Spotify, the channel will likely display a Pink Floyd album based on the consumer’s previous listening experience and habits.
C. Dimensionality Reduction
When the number of features (or dimensions) in a given data set is too high, software engineers use dimensionality reduction techniques. As the name suggests, this technique reduces the number of data inputs to a manageable size without compromising data integrity.
In other words, dimension reduction refers to the technique of reducing the number of input variables in a data set so that the redundant parameters don’t over-represent the impact on the outcome.
Semi-supervised machine learning offers a bridge between supervised learning and unsupervised learning. It uses smaller labelled data to guide classification and feature selection from a larger unlabeled data set.
Learning problems of this type are challenging as neither supervised nor unsupervised learning algorithms can effectively use the mixtures of labelled and untellable data. As such, you will require specialised semi-supervised learning algorithms for such conditions.
Semi-supervised ML operates with two learning approaches: self-training and co-training.
Reinforcement machine learning uses algorithms that aren’t trained to sample data. The model learns as it goes by using trial and error. In other words, reinforcement learning is a feedback-based model that performs the actions and observes the results of such actions.
While other ML techniques learn by passively taking input data and finding patterns within it, reinforcement learning uses training agents to actively make decisions and learn from their outcomes.
The training agents make a decision based on the positive and negative reward points, and over many iterations, they learn to choose the action based on its current state. This technique is commonly used in deep neural networks.
A great example of this method in the real world is self-driving cars. Autonomous driving has several factors, such as the speed limit, drivable zones, collision avoidance, etc. Using reinforcement learning forms, software engineers can teach a system how to drive by avoiding collisions, and following the speed limit, among other things.
The use case of machine learning in the real world is growing across industries. This includes ML in agriculture, of all things! Here’s a look at the major real-world applications of machine learning.
In the world of finance, ML can help tremendously. From enhancing the security of financial transactions to sentiment analysis of stock market trade, ML has a wide range of applications in this area.
Machine learning techniques are known for fraud detection, such as credit card fraud, which improves transactional and financial security. Here, deep learning solutions in Python or R can predict fraudulent behaviours. Such solutions work in real-time, constantly checking the possibility of fraud and generating alerts. Here, classification algorithms work as they label the events as “fraudulent” and “not fraudulent.”
Also, organisations worldwide are using ML to model sentiment analysis for stock market predictions. By using natural language processing, classification and clustering techniques, ML can classify stock variations into three categories: positive, negative, and neutral. For instance, Kavout, a stock trading firm based in Seattle, uses the K-score algorithm model for sentiment analysis, price prediction, and further stock price recommendations.
Moreover, various budget management applications use it to help customers keep track of their expenses, determine their spending patterns, and provide recommendations for better savings. ML algorithms can then help customers improve their financial portfolios according to their income level, expenses, spending habits and preferences.
In the area of cybersecurity, ML can detect cybersecurity attacks by conducting real-time email monitoring. Natural language processing analyses the email content and determines the possibility of a phishing attempt.
Likewise, it can also effectively fight against malicious bots, which can cause cybersecurity attacks such as data breaches, malware attacks, and other threats. Since it is no longer possible to deal with bots using traditional methods, machine learning algorithms are an effective way of fighting against them. For instance, ML-based Twitter Bot identification systems use supervised ML to identify and classify good and bad bots.
Also, the loopholes in browser plugins or similar vulnerabilities make it easy for the attackers to redirect the users to a malicious website and download the malware. ML can control such forms of malware attacks by using convolutional neural networks. Such neural networks can effectively control these forms of cyber attacks and bring down malicious plugins that might otherwise exploit browser vulnerabilities.
ML techniques are effective in marketing as well. One such use case is customer journey optimisation. The primary idea of the process is to optimise the customer acquisition cost to a specific conversion point. Data-driven approaches are now popular in conducting customer journey optimisation. Machine learning algorithms determine all the customer paths and provide a score for each path while considering customer acquisition costs and customer lifetime value as the factors.
In current digital marketing, it is imperative to curate more precise and relevant content. Machine learning curation tools make it easy for marketers to do content curation. For example, Curata and Vestorly help marketers curate content according to the customer’s liking and preferences, thereby significantly impacting the ROI.
Additionally, ML can enhance the customer experience in many ways. For example, interactive chatbots use it along with AI technologies to lead to higher customer engagement. Because of its 24×7 availability, customers these days prefer chatbots to get clarifications on queries about a particular product or service. One example is the machine learning recommendation system, which greatly enhances the overall customer experience and helps companies retain and engage customers.
In the healthcare industry, convolutional neural networks are extensively used to recognise and classify images. That’s why it is being used in detecting skin cancer with high accuracy rates of up to 95% by using TensorFlow. The ML models use hundreds of thousands of images of benign and malignant skin lesions to provide accurate outcomes.
ML techniques can also be used in pandemic management. For example, COVID-19 mortality risk prediction is a machine learning use case in healthcare. Such timely prediction can reduce mortality rates with effective resource allocation and treatment. One such example is the support vector machine that is used for predictive modelling leveraging the patient’s clinical information. The prediction rates are high; eventually, the algorithm can save hundreds of thousands of lives.
Besides predicting diseases and mortality, ML can also do cumbersome administrative tasks for the healthcare industry. This can heavily reduce the physicians’ workload and improve the quality of care as physicians can better concentrate on the patient’s health. Electronic health records are highly critical in healthcare, and NLP tools can make it easy to categorise and label the data automatically. Moreover, such tools can generate visual charts and graphs for physicians to better understand patient health.
The demand for eCommerce is at an all-time high. That’s why recommendation engines use ML, data science, and AI technologies to provide eCommerce platforms with a competitive advantage.
These engines use ML, data science, and artificial intelligence to simultaneously analyse the online activities of hundreds of thousands of users in real-time to provide them with the most accurate and relevant information about products and services. That’s why many eCommerce chains, such as Amazon or eBay, use Python-based machine learning for personalised recommendations for customers.
ML can also help businesses in the dynamic pricing of a product or service, making it easy for customers to map the best price for each product. In this regard, Amazon is one of the leading players in the e-commerce industry. It uses an ML-based dynamic pricing model as the product prices update at a gap of 10 minutes. This is 50 times more than the major competitors, such as Walmart or Best Buy.
In addition, eCommerce sites and retail stores must maintain a perfect balance between demand and supply, i.e. things customers are buying and the stock they have in their warehouses. Procuring products that don’t meet customer demands or are not in demand anymore can result in enormous losses for the company. Here, ML can help in demand forecasting and stocking. Companies can use regression analysis and time series techniques to predict expected sales in specific periods.
Machine learning powers many of today’s most innovative technologies, from the predictive analytics engines that generate shopping recommendations on Amazon to the artificial intelligence technology used in countless security and antivirus applications worldwide. But that doesn’t mean it doesn’t come with some shortcomings or limitations.
The best thing about ML is its ability to review large amounts of data sets and identify patterns and insights that might otherwise be not apparent. For instance, machine learning algorithms can pinpoint the relationship between two events or variables — and help businesses make data-driven decisions. This makes it highly effective at data mining on a continual or ongoing basis.
Another great thing about ML is that it improves over time. The system keeps processing large volumes of data, which gives the algorithm more “experience” to make better decisions or predictions. An example is weather forecasting, and how each forecast in the future comes with greater accuracy because future forecasts are made based on historical data and past records.
Also, ML lets you adapt without human intervention. For example, the security and antivirus software that implements filters and other safeguards in response to new threats. They use machine learning to identify new threats and hazards — and later help neutralise and protect the system against the threat. It practically eliminates the gap between the identification and elimination of a threat.
While machine learning is an excellent invention for humankind, there’s a high level of error susceptibility. One error can lead to another, and because everything is linked in a causal chain, a single error can be fatal to the entire system.
If an error-ridden or inaccurate data set is fed to the ML system, everything the algorithm generates is flawed. In real life, this could be a situation where you have furniture, pet foods, and ebooks all in “related products” when you search for the latest New York Times bestselling novel. Because something is already flawed, the algorithm cannot distinguish between right and wrong — this is where human intelligence is required. So one has to be very careful of all the errors that might otherwise plague the data sets.
Likewise, ML takes time, especially when you have limited computer resources. Handling big data and running parallel computer algorithms can be costly. Therefore, before turning to ML, it is essential to consider whether your company can invest the amount of time and money required to develop the technology.
Another con — well, this could also be a pro, depending on the situation — is automation. If the system is configured to automatically implement improvements as suggested by ML algorithms, sometimes operations might run off the rails until a software engineer comes there for intervention. That is to say, like many other new-age technologies, machine learning is not the answer to every problem, and it isn’t for each company or software application.
Without a doubt, machine learning is everywhere. And for those who want to make a career out of it, you might want to invest some time and energy in learning the following programming languages.
Python is one of the most popular programming languages for its simplicity and readability. Although machine learning algorithms are complicated, Python makes it easy for developers to create the best solutions for the project they are working on.
The best thing about Python is that it supports a wide variety of frameworks and libraries, allowing you to be more flexible and create endless possibilities. To tackle the problems or issues in the project, developers can choose a bunch of Python libraries such as scikit-learn, OpenCV, TensorFlow, PyTorch, Keras, and NLTK, among others. Additionally, Python is the primary choice of programming language for natural language processing and sentiment analysis.
The R programming language is known for its machine-learning support. It focuses mainly on numbers and prioritises data sampling, model evaluation, and data visualisation. R will be among the best choices if your developers need to deal with large volumes of data.
A machine learning engineer can use R to better understand and streamline the statistical data so that it doesn’t become overwhelming. What’s more, R has an enormous number of ML algorithms and advanced implementations written by the developers of the algorithm. Developers can explore, model, and prototype with R. Also, neural networks and neural network libraries, non-linear regression, advanced graphing, and linear algebra are all included in R’s packages.
Java is a popular programming language that is also used in machine learning and artificial intelligence applications. Java has a lot of frameworks that are great for ML, which allows you to build your own machine-learning programs. Java’s strength lies in its ability to generate array-backed support systems for desktop, web, and mobile applications.
Java is most suitable for areas including financial analysis, network security, and cybersecurity. Because of its versatility, many companies prefer Java and want to develop enterprise applications by leveraging its tools and functionalities.
C++ is another programming language that’s used in machine learning. Embedded systems and electronic engineers use it on near-the-hardware scalable ML projects, such as IoT edge analysis. In other words, C++ is a general-purpose programming language that is used for many different types of applications.
It has a reputation for being a difficult language to learn, but it is actually relatively easy once you have the basics down. This programming language is commonly used in ML because it is efficient and effective. Some machine learning projects using C++ include industrial maintenance, image processing, robot locomotion, IoT, and AR/VR. Another use of C++ is to enhance existing machine-learning applications and projects.
Shell is a versatile programming language that can be used to develop machine learning algorithms, models, and applications. By using mathematical models, Shell collects and prepares large amounts of data. The best thing about Shell is that developers can easily process data with its powerful, text-based interface.
Shell is available for all operating systems, including macOS, Windows, and Linux. It comes with various libraries — for example, ML-notebook, DL-machine, and Docker prediction — that can be utilised in ML.
So there you have it — a comprehensive guide to understanding the tech behind machine learning and its impact on the business world.
As ML becomes increasingly popular among the myriad of business technology options available today, companies are quickly realising the promises and challenges of this innovative technology. It’s evident that machine learning will change how business is done — and awareness of all the latest developments and their adaption/adoption will gain you a competitive advantage.
While ML can help businesses automate tasks and improve efficiency, there’s always the potential for misuse or abuse of this technology. Ensure you understand the limitations of technology before making changes to your way of doing business.
To stay ahead of the curve, businesses need to understand both the promises and challenges of machine learning to make informed decisions. If you need more information about ML and its promises and challenges, including how it can be implemented and leveraged to upgrade your business, feel free to reach out to us for a friendly chat. We would be happy to hear from you!