What Is Machine Learning? Benefits And Unique Applications

Imagine a world where computers can learn and adapt on their own. No longer stuck doing just what we program them to do, machines will be able to understand, analyze, and even predict how people behave. This isn’t just a dream; it’s a reality we are quickly moving toward.

In today’s information-filled world, the amount of data can be overwhelming. While it’s easy to collect data, the real challenge is finding useful insights from all that information. This is where machine learning comes in.

Various machine learning tools help developers build and deploy intelligent systems. These tools enable companies to guess what products customers are most likely to buy and what online content they will enjoy.

One common use of machine learning is in recommendation systems. Big companies like Google, Netflix, and Amazon use these systems to learn about what users prefer, helping them offer personalized product and service suggestions.

History of machine learning

Machine learning has been around for quite some time, and it's evident in the way we refer to computers today—"machines" is a term that's become less common.

Below is a brief overview of the evolution of machine learning, tracing its journey from inception to widespread application.

• Pre 1920s: Thomas Bayes, Andrey Markov, Adrien-Marie Legendre, and other acclaimed mathematicians lay the necessary groundwork for the foundational machine learning techniques.
• 1943: The first mathematical model of neural networks is presented in a scientific paper by Walter Pitts and Warren McCulloch.
• 1949: The Organization of Behavior, a book by Donald Hebb, is published. This book explores how behavior relates to brain activity and neural networks.
• 1950: Alan Turing tries to describe AI and questions whether machines have the capabilities to learn.
• 1951: Marvin Minsky and Dean Edmonds built the very first artificial neural network.
• 1956: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon organized the Dartmouth Workshop. The event is often referred to as the "birthplace of AI," and the term artificial intelligence was coined in the same event.

• 1965: Alexey (Oleksii) Ivakhnenko and Valentin Lapa developed the first multi-layer perceptron. Ivakhnenko is often regarded as the father of deep learning (DL).
• 1967: The nearest neighbor algorithm is conceived.
• 1979: Computer scientist Kunihiko Fukushima published his work on neocognitron: a hierarchical multilayered network used to detect patterns. Neocognitron also inspired convolutional neural networks (CNNs).
• 1985: Terrence Sejnowski invents NETtalk. This program learns to pronounce (English) words the same way babies do.
• 1995: Tin Kam Ho introduces random decision forests in a paper.
• 1997: Deep Blue, the IBM chess computer, beats Garry Kasparov, the world champion in chess.
• 2000: The term deep learning was first mentioned by neural networks researcher Igor Aizenberg.
• 2009: Fei-Fei Li launched ImageNet, a large image database extensively used for visual object recognition research.
• 2011: Google's X Lab developed Google Brain, an artificial intelligence algorithm. Later this year, IBM Watson beat human competitors on the trivia game show Jeopardy!.
• 2014: Ian Goodfellow and his colleagues developed a generative adversarial network (GAN). The same year, Facebook developed DeepFace, a deep-learning facial recognition system that can spot human faces in images with nearly 97.25% accuracy. Later, Google introduced a large-scale machine learning system called Sibyl to the public.
• 2015: AlphaGo becomes the first AI to beat a professional player at Go.
• 2020: Open AI announces GPT-3, a robust natural language processing algorithm with the ability to generate human-like text.

Machine learning vs. deep learning

While both machine learning and deep learning are subsets of artificial intelligence, they differ in their scope and complexity.

ML involves training models on data to make predictions or decisions using various techniques, such as decision trees, support vector machines, and k-nearest neighbors. In this approach, human intervention is often necessary to identify relevant features and ensure the models improve over time.

In contrast, DL is a more advanced subset of ML that utilizes artificial neural networks inspired by the human brain, comprising layers of interconnected nodes (neurons). DL models excel in processing vast amounts of data and can automatically identify crucial features without human guidance.

For instance, in image recognition tasks, deep learning can autonomously detect edges, shapes, and complex objects, while traditional ML methods typically require humans to specify these features in advance.

How machine learning works

At its core, machine learning algorithms analyze and identify patterns from datasets, then use that information to make improved predictions on new, unseen data. This process mirrors how humans learn and improve. When we make decisions, we often rely on past experiences to better assess new situations. Similarly, a machine learning model examines historical data to make informed predictions or decisions.

To simplify the concept, imagine playing the dinosaur game in Google Chrome (the one that appears when there's no internet). The challenge is to jump over cacti or duck under birds. A human learns this through trial and error, quickly recognizing that you need to avoid obstacles to stay in the game.

A machine learning application would learn in a similar way. A developer could program the application to jump whenever the T-Rex encounters a dense area of dark pixels, with the success rate of that action increasing over time. By encountering more obstacles and adjusting based on outcomes, the AI could refine its predictions on when to jump or duck.

Let’s take another example:

Consider this sequence:
3 → 9
4 → 16
5 → 25

If you were asked to predict the number that pairs with 6, you’d likely say 36. You did this by recognizing a pattern (each number is squared). A machine learning model works in the same way—analyzing previous data to make predictions based on patterns.

At its essence, machine learning is pure mathematics. Every machine learning algorithm is based on mathematical functions that are tweaked as it learns. This means the learning process itself is rooted in math—transforming data into actionable insights.

4 machine learning methods

There are numerous machine learning methods by which AI systems can learn from data. These methods are categorized based on the nature of the data (labeled or unlabeled) and the results you anticipate. Generally, there are four types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning

1. Supervised learning

Supervised learning is a machine learning approach in which a data scientist acts like a tutor and trains the AI system by feeding basic rules and labeled datasets. The datasets will include labeled input data and expected output results. In this machine learning method, the system is explicitly told what to look for in the input data.

In simpler terms, supervised learning algorithms learn by example. Such examples are collectively referred to as training data. Once a machine learning model is trained using the training dataset, the test data is given to determine the model's accuracy.

Supervised learning can be further classified into two types: classification and regression.

2. Unsupervised learning

Unsupervised learning is a machine learning technique in which the data scientist lets the AI system learn by observing. The training dataset will contain only the input data and no corresponding output data.

Unlike supervised learning, unsupervised learning requires massive amounts of unlabeled data to observe, find patterns, and learn. Unsupervised learning could be a goal in itself, for example, discovering hidden patterns in datasets or a method for feature learning.

Unsupervised learning problems are generally grouped into clustering and association problems.

3. Semi-supervised learning

Semi-supervised learning is an amalgam of supervised and unsupervised learning. In this machine learning process, the data scientist trains the system just a little bit so that it gets a high-level overview.

Also, a small percentage of the training data will be labeled, and the remaining will be unlabeled. Unlike supervised learning, this learning method demands the system to learn the rules and strategy by observing patterns in the dataset.

Semi-supervised learning is beneficial when you don't have enough labeled data or the labeling process is expensive but you want to create an accurate machine learning model.

4. Reinforcement learning

Reinforcement learning (RL) is a learning technique that allows an AI system to learn in an interactive environment. A programmer will use a reward-penalty approach to teach the system, enabling it to learn by trial and error and receive feedback from its own actions.

Simply put, in reinforcement learning, the AI system faces a game-like situation in which it must maximize the reward.

Although the programmer defines the game rules, the individual doesn't provide any hints on how to solve or win the game. The system must find its way by conducting numerous random trials and learning to improve from each step.

Machine learning use cases

Machine learning projects have revolutionized nearly every industry that has undergone digital transformation. Here are just a few of the many impactful use cases of machine learning projects across various sectors.

Image recognition

Machines are getting better at processing images. In fact, machine learning models are better and faster in recognizing and classifying images than humans.

This application of machine learning is called image recognition or computer vision. It's powered by deep learning algorithms and uses images as the input data. You have most likely seen this feat in action when you uploaded a photo on Facebook, and the app suggested tagging your friends by recognizing their faces.

Customer relationship management (CRM) software

Machine learning enables CRM software applications to decode the "why" questions. 

Why does a specific product outperform the rest? Why do customers make a particular action on the website? Why aren't customers satisfied with a product?

By analyzing historical data collected by CRM applications, machine learning models can help build better sales strategies and even predict emerging market trends. ML can also find means to reduce churn rates, improve customer lifetime value, and help companies stay one step ahead.

Along with data analysis, marketing automation, and predictive analytics, machine learning grants companies the ability to be available 24/7 by its embodiment as chatbots.

Patient diagnosis

It's safe to say that paper medical records are a thing of the past. Many hospitals and clinics have now adopted electronic health records (EHRs), which make the storage of patient information more secure and efficient.

Since EHRs convert patient information to a digital format, the healthcare industry gets to implement machine learning and eradicate tedious processes. This also means that doctors can analyze patient data in real-time and even predict the possibility of disease outbreaks.

Along with enhancing medical diagnosis accuracy, machine learning algorithms can help doctors detect breast cancer and predict a disease's progression rate.

Inventory optimization

If a specific material is stored in excess, it may not be used before it gets spoiled. On the other hand, if there's a shortage, the supply chain will be affected. The key is to maintain inventory by considering the product demand.

The demand for a product can be predicted based on historical data. For example, ice cream is sold more frequently during the summer season (although not always and everywhere). However, numerous other factors affect the demand, including the day of the week, temperature, upcoming holidays, and more.

Computing such micro and macro factors is virtually impossible for humans. Not surprisingly, processing such massive volumes of data is a specialty of machine learning applications.

For instance, by leveraging The Weather Company's enormous database, IBM Watson found that yogurt sales increase when the wind is above average, and autogas sales spike when the temperature is colder than average.

Additionally, self-driving cars, demand forecasting, voice recognition, recommendation systems, and anomaly detection wouldn't have been possible without machine learning.

How to build a machine learning model

Creating a machine learning model is just like developing a product. There’s an ideation, validation, and testing phase, to name a few processes. Generally, building a machine learning model can be broken down into five steps.

Collecting and preparing training dataset

In the machine learning realm, nothing is more important than quality training data.

As mentioned earlier, the training dataset is a collection of data points. These data points help the model to understand how to tackle the problem it's intended to solve. Typically, the training dataset contains images, text, video, or audio.

The training dataset is similar to a math textbook with example problems. The greater the number of examples, the better. Along with quantity, the dataset's quality also matters as the model needs to be highly accurate. The training dataset must also reflect the real-world conditions in which the model will be used.

The training dataset can be fully labeled, unlabeled, or partially labeled. As mentioned earlier, the nature of the dataset is dependent on the machine learning method you choose.

Either way, the training dataset must be devoid of duplicate data. A high-quality dataset will undergo numerous stages of the cleaning process and contain all the essential attributes you want the model to learn.

Always keep this phrase in mind: garbage in, garbage out.

Choose an algorithm

An algorithm is a procedure or method for solving a problem. In machine learning language, an algorithm is a procedure run on data to create a machine learning model. Linear regression, logistic regression, k-nearest neighbors (KNN), and Naive Bayes are a few of the popular machine learning algorithms.

Choosing an algorithm depends on the problem you intend to solve, the type of data (labeled or unlabeled), and the amount of data available.

Also, if you want to train the model to make predictions, choose supervised learning. If you wish to train the model to find patterns or split data into clusters, go for unsupervised learning.

Train the algorithm

In this phase, the algorithm goes through numerous iterations. After each iteration, the weights and biases within the algorithm are adjusted by comparing the output with the expected results. The process continues until the algorithm becomes accurate, which is the machine learning model.

Validate the model

For many, the validation dataset is synonymous with the test dataset. In short, it's a dataset not used during the training phase and is introduced to the model for the first time. The validation dataset is critical for assessing the model's accuracy and understanding whether it suffers from overfitting, an incorrect optimization of a model when it gets overly tuned to its training dataset.

If the model's accuracy is less than or equal to 50%, it's unlikely that it would be useful for real-world applications. Ideally, the model must have an accuracy of 90% or more.

Test the model

Once the model is trained and validated, it needs to be tested using real-world data to verify its accuracy. This step might make the data scientist sweat as the model will be tested on a larger dataset, unlike in the training or validation phase.

In a simpler sense, the testing phase lets you check how well the model has learned to perform the specific task. It's also the phase where you can determine whether the model will work on a larger dataset.

The model gets better over time and with access to newer datasets. For example, your email inbox's spam filter gets periodically better when you report particular messages as spam and false positives as not spam.

Best machine learning software

As mentioned earlier, machine learning algorithms are capable of making predictions or decisions based on data. These algorithms grant applications the ability to offer automation and AI features. Interestingly, the majority of end-users aren't aware of the usage of machine learning algorithms in such intelligent applications.

To qualify for inclusion in the machine learning category, a product must:

• Offer a product or algorithm capable of learning and improving by leveraging data
• Be the source of intelligent learning abilities in software applications
• Be capable of utilizing data inputs from different data pools
• Have the ability to produce an output that solves a particular issue based on the learned data

* Below are the five leading machine learning software from G2's Fall 2024 Grid® Report. Some reviews may be edited for clarity.

1. Vertex AI

Vertex AI is a unified platform that simplifies the development and deployment of ML models. It offers a comprehensive set of tools and services, including data preparation, model training, evaluation, and deployment, making it easier for developers and data scientists to build and manage ML applications.

What users like best:

"For a personal project, I decided to build a conversational AI chatbot aimed at making the chat feel more human. I initially used Dialogflow, but the responses didn’t sound natural. I had trouble organizing conversations, planning user flows, and handling errors.

Then, I found the Vertex AI Agent Builder (formerly called Vertex AI Search and Conversations). Using the Agent Builder API saved me a lot of time on authentication and access issues. In the end, I was able to create a chatbot that sounds natural, using a knowledge base I built with LLM and RAG."

- Vertex AI Review, Tejashri P.

What users dislike:

"There is a lack of in-depth documentation for some advanced features and more complex use cases. Additionally, depending on the workload and configuration, training times can sometimes feel slower than when using dedicated hardware for running models."

- Vertex AI Review, Manoj P.

2. Amazon Forecast

Amazon Forecast is a fully managed machine learning service that uses advanced algorithms to generate accurate forecasts for time series data. It leverages the same technology used by Amazon.com to predict future trends for millions of products. By accurately predicting future demand for products and services, businesses can optimize inventory, reduce waste, and improve planning. 

What users like best:

"Amazon Forecast is an easy-to-use predictive analytics service that automatically handles large volumes of data, making it ideal for a variety of forecasting needs. With its advanced algorithms, it generates highly accurate forecasts, helping businesses make informed decisions based on reliable insights."

- Amazon Forecast Review, Annette J.

What users dislike:

"The accuracy and effectiveness of forecasts generated by Amazon Forecast greatly depend on the quality and relevance of the input data. If the historical data used for training includes anomalies, outliers, or other quality issues, it can negatively affect the forecast's accuracy."

- Amazon Forecast Review, Saurabh M.

3. Google Cloud TPU

Google Cloud TPU is a custom-designed machine learning application-specific integrated circuit (ASIC) designed to run machine learning models with AI services on Google Cloud. It offers more than 100 petaflops of performance in just a single pod, which is enough computational power for business and research needs.

What users like best:

"I love the fact that we were able to build a state-of-the-art AI service geared towards network security thanks to the optimal running of the cutting-edge machine learning models. The power of Google Cloud TPU is of no match: up to 11.5 petaflops and 4 TB HBM. Best of all, the straight-forward easy to use Google Cloud Platform interface."

- Google Cloud TPU Review, Isabelle F.

What users dislike:

"I wish there were integration with word processors."

- Google Cloud TPU Review, Kevin C.

4. Jarvis

Jarvis by NVIDIA is a machine learning platform that provides a user-friendly interface for building and deploying ML models. It simplifies the process of data preparation, model selection, training, and evaluation. Jarvis ML offers pre-built models for common tasks like image classification, natural language processing, and time series forecasting.

What users like best:

"Jarvis is similar to other AI technologies, but what I appreciate most about it is its voice command input feature, which enhances productivity. Additionally, it provides creative content suggestions for blog creators, making it a valuable tool for content generation."

- Jarvis Review, Akshit N.

What users dislike:

"The voicing feature is effective, but users accustomed to Google Voice may find the input-to-output fluidity less satisfying compared to other voice options. While the user interface appears visually appealing, it's essential that the API configured behind the UI also performs well."

- Jarvis Review, Adithya K.

5. Aerosolve

Aerosolve is a machine learning software platform designed primarily for predictive analytics and data science applications. It is particularly noted for its ease of use, allowing users to build complex models without requiring extensive programming skills.

What users like best:

"I'm impressed by its advanced capabilities. It's very easy to use, with smooth implementation and straightforward integration. Additionally, the customer support is decent, making the overall experience positive."

- Aerosolve Review, Rahul S.

What users dislike:

"Aerosolve falls short in areas such as image processing capabilities."

- Aerosolve Review, Aurelija A.

AI is the brain, ML is the muscle!

Machine learning is the muscle that allows AI to learn, adapt, and perform complex tasks. From data science to AI engineering, machine learning is being used everywhere. 

As machine learning continues to evolve, we can expect to see even more innovative applications. From self-driving cars to personalized medicine, machine learning is transforming industries and improving our lives.

However, with this progress comes the responsibility to ensure that these technologies are developed and used ethically. By addressing concerns like data privacy and bias, we can harness the power of AI through machine learning and create a more personalized, inclusive, and intelligent future.

 Discover machine learning statistics that will shape the future landscape.

This article was originally published in 2021. It has been updated with new information.