Businesses must understand the technologies that drive innovation to always stay ahead of the curve. With all the digital transformations that are happening all around us, staying updated is more of a necessity than a choice. Machine learning (ML) is the one technology that must be understood and rightly implemented by organizations to ensure success. This blog is on what is Machine Learning dives into the top key aspects of this technology.
Beginning with its definition, tools, examples and how it compares to AI and deep learning, everything's covered. The most important question needs to be answered first and that means understanding 'what is machine learning'.
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If you come to think about it, ML is more widely present than we usually care to estimate. Not sure what we're talking about? How do computers recognize faces in pictures, beat humans at games and translate languages? ML is what goes on behind-the-scenes. But what is machine learning? It is all about teaching computers to engage in self-learning. This is done by feeding a model gigantic amounts of data during the training phase. This prepares it to learn from patterns in new and old data without being programmed explicitly every single time. It has stemmed from artificial intelligence (AI) but thus, these two are not interchangeable terms.
In 2022, the market size for ML was $19.20 billion globally. By 2030, this number is expected to reach $225.91 billion, showcasing a staggering CAGR of 36.2%. These numbers are derived from a report published by Fortune Business Insights.
The history of ML is quite amazing. The first even mention of the term 'machine learning' was by Arthur Samuel in 1959. This IBM employee was a pioneer in AI and computer gaming.
However, it all began in 1943, when mathematician Walter Pitts and neurophysiologist Warren McCulloch wrote a paper on neurons and their working.
The Turing test was created in 1950 by Alan Turing. The very first computer learning program was written in 1952 by Arthur Samuel. In early 1960s, Cybertron, an experimental learning machine, which used rudimentary reinforcement learning, was developed by Raytheon Company.
The world has not looked back since then! ML has grown exponentially and is still said to be in its evolution phase.
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So, how does machine learning work?
ML utilizes a systematic approach for predicting new values by following a set of steps. These steps are
The data quality is imperative in determining the accuracy of the predictions. Data sets can be built-in or collected from websites, APIs, social media, etc.
Missing and duplicate values are removed, format is standardized and outliers are dealt with.
An algorithm is used to model the data set, which is divided into two parts namely training and testing sets. Different models and techniques are used here.
This step determines the accuracy of the model. It is tested via different techniques and metrics.
Trained models are integrated into real-world issues with the aim of solving them. The models are practically used here.
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Let's understand the most common types of machine learning.
Supervised learning, an ML type which involves training a model based on the known output (such as recognizing an image of orange). It revolves around instructing machines on labeled datasets to achieve the desired outcome. In other words, the input data (images of oranges) is fed to machines to instruct them to predict the output accurately.
For instance, a labeled dataset of pictures of apples, oranges, and grapes would have each picture tagged with either 'apple', 'orange' or 'grape'.
Unlike supervised learning, unsupervised learning is based on unlabeled datasets. The machines are not provided with any algorithm with labeled outputs, resulting in no human intervention. This means that the machine has to rely on its own abilities to reveal concealed patterns, clusters, or similarities within the data.
This type of ML merges both approaches of supervised and unsupervised learning. It takes a little bit of labeled data and lots of unlabeled data to instruct models. Its objective is quite similar to supervised learning, as the machine is trained to come up with desired outputs based on the given input.
Reinforcement learning is a way for machines to learn by trying things out and seeing what works and what doesn't. It relies on making mistakes and improving from them. With this method, the system gets better over time by using feedback based on rewards. This approach is commonly used for specific tasks, like in Google's self-driving cars or AlphaGo, where a program competes against humans or itself to improve its game in Go. Every time new data is fed into it, the system learns from that information, which helps it get better and more experienced.
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While understanding the types of machine learning (ML) is helpful, it is just one part of the overall ML puzzle. An algorithm is what powers an ML model; it is the mathematical function that will learn based on the data. Below are four of the most popular types of ML algorithms everybody should be familiar with.
Linear regression is one of the most basic ML algorithms as it is used to forecast continuous numerical values based on historical data. Linear regression does this by finding the best-fit straight line that passes through the data points by calculating the equation for a linear relationship. For example, if you wanted to determine the selling price of a house, you might want to evaluate its size, location (neighbourhood), and age.
Even though it has the same name as a statistical method, it is not used for statistical modelling purposes. Instead, it is applicable to classification tasks that result in two potential outcomes, or binary targets such as whether or not a message is spam, failed,or passed. Logistic regression calculates the probability that a given value will belong to one of two classes.
Decision trees are similar to flow charts in that they are typically made up of nodes, branches, and leaves (or final predictions of classification) based on branching off each other using a defined path. Decision trees are highly visual (easy to follow) and very interpretable; thus, they are popular for business decisions.
Random forests are designed to create multiple decision trees and aggregate their outputs for a more balanced, accurate, and stable prediction. This method also reduces the risk of overfitting that individual decision trees frequently experience.
There is no dearth of features of ML that make it such a sought-after technology. Here are five of those.
ML algorithms automate various processes like pattern finding in data. Consequently, less human involvement is required, which renders the analysis effective and precise.
Data is always changing and updating. Machine Learning algorithms automatically learn and adapt from it continuously. This enhances their performance over time, enabling them to become more efficient and precise.
ML techniques are crafted to process big data easily. This helps them in gaining better insights through gigantic data sets covering diverse information.
ML algorithms use data for curating models with the prowess of forecasting future events. After being trained on humongous data sets, they become better at predicting the likely patterns.
These algorithms gain the capability to discover broad patterns and the likelihood of outcomes, which are then used to analyze new and unexplored data. The goal is generally long term and not short-term.
While ML is a transformative technology, it is not without its hurdles. Organizations and developers must be aware of these limitations before deploying ML systems.
ML models are created using training on large high-quality data to make accurate predictions. It takes a considerable amount of time and money to gather a sufficient amount of the same type of data promptly to ensure accuracy. If the data is of low quality (i.e., contains errors or irrelevant information), then the resulting predictions will not be accurate (commonly referred to as "garbage in, garbage out").
ML data set and training models could reflect historical discriminatory trends (e.g., hiring, lending) and create ML algorithms that will continue to look for outdated cultural practices/standards. These inaccurate measures of "ability to repay" create a large possibility of future trends hurting groups such as individuals with lower education levels, women, and individuals from ethnic and racial minority groups (these groups may have higher rates of negative outcomes in health, criminal justice, and financial services).
Some of the complex and advanced ML models being used today (particularly deep learning) are extremely difficult to interpret, creating challenges for determining the reason why a model made a certain prediction. The lack of interpretability as it relates to accountability will reduce the effectiveness of ML models in high-stakes industries (i.e., medical and legal professions).
Complex ML models require a very high level of computing power, memory, and energy resources to create and train, representing a very large expense for an organization.
There are many similar-looking technologies out there. This can make it a bit difficult to understand how exactly they are different from one another. Since most people confuse these, here's a look into machine learning vs artificial intelligence vs deep learning.
| BASIS | ARTIFICIAL INTELLIGENCE (AI) | MACHINE LEARNING (ML) | DEEP LEARNING (DL) |
|---|---|---|---|
| Concept | A broad field focused on making machines simulate human intelligence and decision-making. | A subset of AI that develops algorithms that learn patterns from data to make predictions. | A subset of ML using layered neural networks to learn complex patterns from massive datasets. |
| Optimal Data Volumes | Varies — works with small to large datasets depending on the approach. | Thousands of data points for effective training. | Millions of data points / big data required. |
| Management | Requires human-defined rules and logic in traditional approaches. | Data analysts guide inputs/outputs; the model then learns without explicit programming. | Largely self-directed; data analysis drives its working with minimal human intervention. |
| Usage | Robotics, expert systems, planning, NLP, and computer vision. | Mostly used for predictions, classifications, and numerical scores. | Wide usage — from free-form generation (Generative AI) to image and speech recognition. |
| Examples | Chess-playing engines, virtual assistants like Siri. | Spam filters, credit scoring, recommendation engines. | ChatGPT, DALL-E, facial recognition systems. |
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To have a comprehensive understanding of machine learning, we must talk about Generative AI specifically; this is the tech behind ChatGPT and other generative tools like Google’s Gemini, DALL-E and Midjourney.
Generative AI is an area of deep learning that can create completely new content including: text, images, audio, code, and video. This technology differs from a traditional machine learning (ML) model in that it produces new outputs that do not exist in their training dataset; ML predicts or classifies an ‘existing’ pattern, however, Generative AI generates a new output from a Previously ‘Existing’ ML Model.
Generative Models (e.g. Large Language Models) are built using ML techniques and trained on large datasets. During the training process, Generative Models learn the statistical structure, grammatical construction, contextual structure, and logically consistent reasoning found within human written text. After training, Generative Models can generate coherent and contextually relevant responses based on new input, by trying to predict what the most likely and useful next word, sentence, or concept would be.
The primary architecture for LLMs, such as GPT4 and Gemini, is the Transformer model, which employs the use of "Attention Mechanisms" to assess the relationship between words in longer sections of text.
GANs (Generative Adversarial Networks) use two competing neural networks. The first creates fake content, and the second attempts to detect whether that content is real or artificial. The competition produces incredibly realistic output. GANs are commonly used in the creation of images.
Diffusion models are the basis for tools like DALL-E and Stable Diffusion. These models have been trained to recreate original images by reversing the noise process that occurs when images age. By reversing this process, the models can produce very high-quality images from text input.
Generative AI technologies have gone from being "just an interesting new idea" to being "essential" for enterprises. Companies that use Generative AI include those engaged in producing content, automating customer service, drug discovery, software development assistance, and building personalized education systems. Understanding how and why generative AI works can only be accomplished by first understanding how machine learning works, and what causes the failures of generative AI systems.
The real world examples of ML are increasing with every passing day. This technology is spearheading into the future with exceptional possibilities and various use cases.
Facial recognition is a big use case and benefit of ML. It has positively transformed various aspects of our everyday tasks and activities. This example, paired with deep learning, is leaving an impact on different industries and sectors. It aids in combating key social issues like detecting thiefs, trafficking, and smuggling. In healthcare, it tracks a patient's history of drug abuse/medication and detects genetic diseases.
Successful implementation of ML leads to email automation and spam filtering. This area is especially in focus for organizations that rely mostly on outbound leads. Spam filtering means getting a better grasp of the patterns that make the email content undesirable.
All big platforms like Facebook, Twitter and Instagram utilize AI, ML and big data to enhance the use experience and offer better functionality. ML has played a big role here in tackling cyberbullying and inappropriate content. Both these risk the platform in losing its integrity and getting a tarnished image. When data is processed via deep neural networks (DNNs), these platforms get a better understanding of their user preferences. This leads to target advertising and relatable content suggestions.
Targeted marketing in retail uses ML to learn about user preferences. Their demographic similarities, buying habits and purchasing history are used to train the models. Depending on how good quality and accurate the data is, the predictions can either be bang on or way off. Not buying or clicking through a product also creates a data point for future reference.
The finance industry is benefitting tremendously with the rise of ML. It's tiring and almost impossible for the human eye to analyze the increasing number of transactions. But with ML, analysis is done swiftly, helping in finding fraudulent transactions. Other uses include assessing credit scores to determine lending decisions and doing risk analysis. AI, natural language processing and ML come together to enhance a customer's banking experience.
ML powers the self-driving technology wherein sensors are used to collect data surrounding the car in real-time. This real-time data is employed to guide the car and navigate its response in varying situations. These could be an animal/ human crossing the road, red light or another vehicle.
Speech signals are supported and manipulated into text and commands by language models. This is pretty similar to how ML recognizes images. A software app that's coded with AI has the potential to convert live and recorded speech into text files.
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Machine learning is changing every industry around the globe by providing companies with automated process, and better decision-making, and smart service delivery. From agriculture to healthcare to finance to education, machine learning is creating new ways of functioning more effectively, accurate, and ultimately providing a better user experience across industries.
Machine learning is currently being implemented in agriculture to give farmersly forecast the amount they will grow (crop yield), measure the health of their soil, and find out what the weather will be. Machine learning-assisted programs will use data from satellite photos taken above the earth, historical climate data for a crop-growing region, and traditional crop-rotation patterns, and analysis of farmer-to-farmer farming methods (techniques) to help farmers predict where, how, and when to introduce irrigation, fertilize their crops, and apply pesticide, and ultimately produce and secure enough food to help feed the world.
Machine learning is being used in banking as a way for banks to detect fraudulent transactions in real time; allow banks to assess their own risk levels, fraud, mitigate risk; and improve the safety and reliability of digital banking for their consumers.
Machine Learning technologies are used to assist healthcare organizations to detect diseases utilizing medical images, predict risks of being a patient (from a risk perspective); and assist in more rapid diagnoses. Additionally, advanced machine learning technologies are helping healthcare providers detect specific healthcare conditions such as cancer, diabetes, and heart failure more accurately, and ultimately assist them in determining better and more effective treatment options.
By implementing machine learning into smart city systems and governments, many improvements can occur in areas such as traffic management, public safety, and waste disposal. Machine learning can also help analyze large amounts of public data to better create plans for future cities, decrease traffic, and enhance the quality of services in cities.
ML is not just a technology trend; it is one of the fastest-growing career fields in the world. As organizations across every industry race to adopt AI-driven solutions, the demand for skilled ML professionals has never been higher.
| Job Role | Primary Responsibilities |
|---|---|
| Machine Learning Engineer | Designs, builds, and deploys ML models into production systems. |
| Data Scientist | Analyzes data, builds predictive models, and extracts business insights. |
| AI Research Scientist | Develops new ML algorithms and techniques, typically in research settings. |
| MLOps Engineer | Manages the lifecycle of ML models — deployment, monitoring, and retraining. |
| NLP Engineer | Builds models that understand and generate human language. |
| Computer Vision Engineer | Develops models for image and video recognition tasks. |
Machine learning careers require strong technical, analytical and problem-solving skills to build intelligent systems and work with data effectively.
Having reliable machine learning tools and technologies by one's side is integral to ensure success. It's a long road to gain prowess in all the top ones but now's a great time to get started.
Microsoft Azure ML is a 360-degree managed cloud service. It helps developers and data scientists in deploying, building and managing the entire ML project lifecycle. All this happens faster and with great precision. The time to value is accelerated with MLOps, integrated tools and open-source interoperability.
Key feature include-
Amazon SageMaker is a popular, completely managed service that's crafted to build ML models and generate predictions. This platform is used by developers for building, deploying and training the ML models at large. It's done in a single IDE (integrated development environment) by using different tools. Governance requirements are supported via transparency and simplified access control over the ML project. It's particularly beneficial because of its wide array of tools and multi-framework support.
It's key features are-
PyTorch refers to an optimized and open-source tensor library. It is most often used to act as a support pillar in developing DL models with GPUs and CPUs. It boasts a vibrant community, rendering a great learning experience. Its dynamic computation graph enables the models to create and modify on the fly.
Top features are-
Apache Mahout is a distributed, open-source linear algebra framework. Additionally, it's a mathematically expressive Scala DSL (domain-specific language). It's implemented on Apache Hadoop and crafted for mathematicians, data scientists and statisticians primarily. The biggest perk of Apache Mahout is its scalability. Different professionals employ this framework for building efficient and scalable implementations rapidly.
Key features include-
TensorFlow is an open-source and 360-degree ML platform by Google. It is primarily associated with the inference and training of deep neural networks. Additionally, it provides plenty of libraries and tools like TensorFlow Serving, enabling users to train, deploy and build ML models.
Key features of TensorFlow are-
Weka is an accumulation of ML algorithms for various data mining tasks. It comprises plenty of tools for data visualization, preparation, clustering, association rules mining, regression and classification. This one platform aids companies in processing, managing and storing their data both on premises and in the cloud.
Key features of Weka include-
ML is an amazing technology that equips systems with the prowess to learn from old and new data. This eliminates the need for explicit programming every single time. It's a huge technology with endless possibilities and use cases. This blog covers the definition of 'what is machine learning' along with its real-world examples, tools and technologies. Becoming a ML engineer is a great career option. To launch a career here, good training holds utmost importance.
Since there is no one specific certification in this field, any good, widely recognized credential can be chosen. In fact, certification is not even a prerequisite to get a job. Expertise, knowledge and skills are the biggest counting factors.
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In reference to AI, ML is a subset that focuses on allowings systems to learn from data and improvise their performance without using extensive programming. In simple words, it is about teaching computers to learn from data, identify patterns and make predictions or decisions.
Natural Language Processing is another subset of artificial intelligence that powers the machine learning tasks. It enables computers to understand, interpret and generate human language.
There are several examples of ML in our day-to-day life, including:
Artificial Intelligence is about making machines think like humans, while machine learning is a part of AI that helps machines learn from data.
Machine Learning teaches machines to learn from data, while deep learning uses neural networks to learn complex patterns from large datasets.
