What is machine learning, how does it work, and what are the different types of “ML” available to businesses today? Machine learning is one of the most significant branches of artificial intelligence (AI) and computer science in the last few decades.
Machine learning systems leverage data and algorithms to allow AI systems to learn, improve, and become more effective over time. Essentially, it lets machines “learn” like human beings.
Today, machine learning is one of the fastest-growing components of artificial intelligence. The ML market is currently growing at a CAGR of 34.8%. Without machine learning, we wouldn’t have the most exciting AI solutions today, such as generative AI bots and conversational AI systems.
Here’s your educational guide to machine learning.
What is Machine Learning?
Machine learning is a branch of computer science and AI that allows machines to imitate human learning processes. The overall goal of artificial intelligence development is to create computer models that can simulate human behaviors.
For instance, we have AI systems that can complete tasks like entering data into an ecosystem, recognizing images with computer vision, understanding text, and even processing audio. However, AI systems need to be “trained to act like human beings.”
While old-fashioned AI models can only ever leverage the data and rulesets they’re given to complete tasks, AI solutions with machine learning can learn and improve over time. They can analyze the patterns between data, make predictions, and grow more effective without as much hands-on training.
The Difference Between AI and ML
The simplest way to differentiate between Artificial Intelligence and Machine Learning is to understand that AI is a broader concept. Artificial Intelligence enables a machine to reason, act, sense, or adapt like a human being, while Machine Learning gives AI new abilities.
It allows machines to extract knowledge from their given data, make connections, and improve themselves autonomously. Machine learning falls under the broader umbrella of AI and is associated with various other subfields, such as robotics, deep learning, and expert systems.
How Does Machine Learning Work?
Machine learning relies heavily on data and carefully designed algorithms. Although AI systems with machine learning features can improve by themselves over time, they still need to be trained. This means giving them access to the data they’ll use to complete tasks and showing them how to connect data sets.
Training is essential to reduce the risk of machine learning systems making process mistakes. Developers who create ML algorithms use several steps to train their systems. This starts with data collection (gathering all the information the system needs) and data pre-processing, which involves cleansing and normalizing data before it’s fed into the algorithm.
Once they have their data prepared, developers choose their ideal ML model. There are various options, such as linear regression models, neural networks, and decision trees. The model is fed with the data and regularly evaluated to see how accurately it can understand the patterns in that data. Various methods are then used to tune the model and enhance its performance.
We’ll cover the common machine learning models and training methods in more depth in a minute. However, the important thing to remember is all machine learning models need:
- A decision process: An algorithm that allows machine learning models to classify data, make predictions, and understand patterns in labelled and unlabelled data.
- Error functions: An error function allows developers to evaluate the prediction accuracy of the model and improve their performance over time.
- Model optimization processes: Machine learning models use “evaluate and optimize” processes to gradually optimize performance.
Machine Learning vs Deep Learning and Neural Networks
One reason many people struggle with the question, “What is machine learning?” is that AI algorithms and models are constantly evolving. These days, terms like “machine learning,” “deep learning,” and “neural networks” are often used interchangeably. However, there are differences between them—even though they’re all sub-fields of artificial intelligence.
Neural networks are a sub-field of ML, while deep learning algorithms are a sub-field of the neural network landscape. The biggest difference between machine learning and deep learning is how the algorithms actually learn.
Deep learning systems can use supervised learning strategies (labelled datasets), and unsupervised learning (unlabelled datasets). They can ingest unstructured data and automatically determine features that differentiate categories of data. Classic ML systems, on the other hand, require more human intervention to enable their learning process.
Neural networks, notably, are systems of node layers, containing hidden layers, input layers, and output layers. Each node connects to another and possesses its own threshold and weight. If nodes are activated, they send data to the next layer of the network. In deep learning, neural networks have multiple layers (more than 3), allowing faster, more nuanced learning processes.
Deep learning is part of what’s driving significant development in areas like natural language processing and computer vision for bots.
What is Machine Learning? ML Methods
Machine learning models come in various forms, often differentiated by how they’re trained or developed. There are three common “types” of ML methods:
Supervised Learning
Supervised machine learning models are created by developers that use labelled datasets to teach algorithms how to classify data and predict outcomes. Input data is fed into the model, and the model adjusts its weight to understand the value of that data.
With supervised learning, both the input and output of an AI model will be specified by human developers. Although this form of ML might seem limited, supervised learning algorithms are used for countless tasks. They can enable binary classification, multi-class classification, regression modeling, and ensemble modeling.
Unsupervised Learning
Unsupervised machine learning models use algorithms to analyze and categorize unlabelled data sets. The algorithms discover hidden patterns in groups of data without human direction. In unsupervised learning, models use methods like gradient descent, principal component analysis, and singular value decomposition to understand data.
Most deep learning solutions use unsupervised learning algorithms, including neural networks. Unsupervised learning allows models to specify data into groups based on similarities, identify unusual data points in a set, and even automatically reduce the number of variables in a data set.
Semi-Supervised Learning
Semi-Supervised machine learning is a hybrid of the unsupervised and supervised strategies. During training, teams use smaller labeled data sets to help the model understand data and complete certain tasks. The model uses this small data set to guide its understanding of larger, unlabelled data sets. This can be a great way to develop a bot when there’s limited data for a fully supervised process.
Notably, though, providing too little training data to a model can sometimes lead to problems, such as “overfitting”, where a model just memorizes the data set rather than understanding the underlying patterns. Semi-supervised learning is used for everything from developing bots for machine translation to fraud detection.
Reinforcement Learning
Reinforcement machine learning is similar to supervised learning, as it still involves significant human input. However, the algorithm in this case isn’t trained with sample data. Instead, it uses “trial and error”, to learn as it grows. This is the type of training that powered IBM’s Watson application when it was trained to win the Jeopardy challenge in 2011.
Reinforcement learning is an effective way to teach robots how to perform tasks in the physical world or complete specific processes (like playing a game).
Common Machine Learning Algorithms
Just as there are different methods developers can use to train ML systems, there are also various “algorithms” involved in machine learning. Some of the most common include:
- Linear regression: An algorithm that predicts numerical values in a data set by examining the linear relationship between various values. For instance, a system might use historical data about house prices to predict future prices for homes in a certain area.
- Logistic regression: This algorithm makes predictions for categorical response variables, like “no/yes” answers to queries. It’s commonly used in AI-powered spam filters, and in quality control systems in the manufacturing space.
- Neural networks: A neural network uses multiple nodes to simulate how human brains work. They’re excellent at recognizing patterns, and can enable systems to translate and process natural language, understand images, and even create images with generative AI.
- Clustering: In unsupervised learning, clustering algorithms identify patterns in data to create data groups. They examine the similarities in different data sets for categorization.
- Decision trees: Decision trees help ML models classify data into categories, predict numerical values and more. They use branching sequences of linked decisions, represented with a tree diagram.
- Random forests: With a Random Forest algorithm, machine learning systems predict a category or value by combining results from various decision trees. This allows them to adapt to a wider range of scenarios.
Why is Machine Learning Important?
So, why does machine learning matter? Simply put, this technology has had a huge impact on human beings for years now. Training machines to learn from the data they’re given and improve over time has paved the way for extensive automation in the business landscape.
Machine learning also allows computers to complete manual tasks that would be difficult for human beings to manage at scale. For instance, they can process huge quantities of data in minutes and extract patterns and insights faster than any human being.
ML gives us an opportunity to understand and use the ever-growing volumes of data we’re generating as human beings. It also ensures we can effectively develop the next generation of AI, powering solutions like conversational and generative AI bots.
On top of that, ML supports a wide range of versatile use cases, including:
Improving Business Intelligence and Insights
Business Intelligence and Predictive Analytics tools regularly use machine learning algorithms like logistic regression and linear regression to identify and categorize data points. They can rapidly spot patterns and anomalies in data sets, to help businesses make better decisions, and even forecast future trends and opportunities.
ML systems can even be used to create recommendation engines for business leaders, using historical data and insights into trends to identify cross-selling, upselling, and sales opportunities. This can boost revenue for companies in a range of industries.
Speech and Image Recognition
Without machine learning, we wouldn’t have the speech recognition tools that enable smart interactive voice response systems, AI virtual assistants, and smart assistants. These tools use natural language processing to translate human speech into a written format for bots to understand.
ML also helps bots to recognize other forms of data, such as images and objects in videos. With computer vision, systems can pull meaningful data from all kinds of visual inputs, using neural networks to tag different elements. This type of technology is common in everything from the healthcare sector, to the security landscape. It even helps power autonomous cars.
Enhancing Customer Service
Machine learning is part of what makes the latest chatbots and virtual assistants, used for self-service strategies, so effective. Without ML, bots could only ever respond to questions based on the data they already had. However, with ML algorithms, bots can learn about customers and common problems, deliver more personalized service, and suggest creative solutions to problems.
ML helps to enhance the generative AI and conversational AI bots used in various aspects of customer service. It can even support employees in delivering better service, with real-time training, coaching, and suggestions based on historical and immediate data.
Upgrading Automation
As mentioned above, machine learning has paved the way to an era of hyper automation in the business landscape. With ML, companies can automate a wide variety of tasks, from data input to meeting summarization and even customer outreach.
Machine learning allows systems for automation to learn over time, becoming more efficient and effective at completing countless tasks. This means we can now use automated systems for everything from automated stock trading in the financial sector, to supply chain management in logistics. Some solutions can even use predictive analytics, help companies proactively automate certain tasks.
Boosting Compliance and Security
Machine learning algorithms power numerous tools used to improve data security, compliance, and privacy in the business landscape. It can enable systems to use biometric data to authenticate users, and enhance fraud detection processes, by scanning interactions for suspicious activity.
ML systems can also understand how various aspects of a business work, from network connections, to calling systems. They can monitor these environments in real-time and instantly alert team members when issues arise, or implement autonomous fixes.
The Problems with Machine Learning</h2></h2></h2>
While there are numerous benefits to machine learning in the modern world, there are still challenges to overcome. First, there’s the growing concern around “technological singularity” to consider. Although we’re still a long way from AI systems having the same level of awareness as human beings, machine learning does introduce one of the tools we need to create super-intelligent systems.
If we can teach machines how to learn autonomously, there’s a chance one day we’ll be able to show them how to think for themselves. Additionally, like many forms of AI, machine learning has led to concerns around human employment. Because ML allows for the automation of many different tasks, there’s a chance that bots could replace human beings in various job roles.
Still, many experts agree that both AI and machine learning will simply change the responsibilities workers have, reducing their focus on repetitive tasks.
Then, we also have privacy, security, and ethical issues to consider. Like all forms of AI, ML systems need to be trained with vast amounts of data. Ensuring that data can’t be accessed by outside parties, and preventing systems from accessing sensitive data can be complicated.
Plus, machine learning can also lead to issues of bias and discrimination when data sets are limited. Similar to many forms of artificial intelligence, ML algorithms can make false assumptions and make mistakes when understanding connections between data sets.
Finally, certain types of ML algorithms, like neural networks, are difficult to understand and explain, which leads to issues with accountability and transparency.
The Future of ML and AI
Machine learning is an incredible subset of artificial intelligence, responsible for powering most of the AI innovations we know today. It’s a crucial pillar in all of the world’s most powerful conversational and generative AI apps.
As developers continue to experiment with new training methods and models, ML algorithms will only become more accurate and effective. While there are certainly challenges to overcome in this landscape and concerns with ethics and privacy to address, the evolution of machine learning could also lead to incredible developments worldwide.
