What used to be a plot theme for an old science fiction movie, artificial intelligence (AI) has become a modern reality. But AI isn’t just the robotic humanoid that becomes self-aware and comes to save the world. In fact, we use AI every day. From the facial recognition we use to open our smartphones to streaming service show recommendations, you’re seeing AI hard at work — and at the core of AI is machine learning. So, what is machine learning?
An Overview
Let’s start by using Netflix as an example: the company’s recommendation engine is powered by artificial intelligence and uses stored historical data to send you suggestions of what you might be interested in watching — right down to your favorite actors. Learning to code sets the stage for developing machine learning systems, not unlike the one used by Netflix.
After you’ve unlocked your phone (maybe even with facial recognition), how often do you pull up your Facebook, X (Twitter), Instagram, or Pinterest? And when you do, how do you think it was updated overnight? Not only is artificial intelligence working behind-the-scenes to personalize your feeds, but it’s also determining friend suggestions and pages you may like to follow. With a coding background, working on social media engines and their machine learning makeup is within reach.
Pattern recognition and the idea that computers can learn independently led to the development of machine learning. The history of machine learning dates back to the 1950s when Alan Turing created what’s known as the “Turing Test” — a simple method of inquiry that determines whether or not a machine can exhibit human intelligence. When a machine can engage in a conversation with a human without being detected as a machine, it has demonstrated human intelligence.
In the software development industry, machine learning is a subfield of AI and computer science that provides systems with the capacity to innately learn and evolve from experience without being explicitly programmed by humans. Machine learning uses meticulously designed statistical strategies and algorithms that allow a computer to imitate the behavior of human brains with its deep neural networks and problem-solving skills. Machine learning aims to develop computers that learn from previous computations to make accurate decisions and produce accurate results.
How It Works
Machine learning begins with historical data in the form of numbers, images, symbols, clicks, etc. Things as simple as grocery lists, addresses, bank transactions, sales reports, and previously purchased items, are all data that can be harvested for use.
Once the initial data is obtained, it is then analyzed and prepared so the machine learning model can be properly trained to operate independently. As data is accrued, it provides more opportunities for the model to be tested and corrected.
Moving forward, programmers decide which learning model they wish to proceed with and implement the data so the computer can continue learning on its own.
Depending on the computer’s output, the data is tested against potential error functions to evaluate model predictions. When an error function is observed, the computer can compare and assess the accuracy of the model.
Like human brains, machine learning computers work through a great deal of trial and error to produce precise results. Once inconsistencies are acknowledged, the algorithm will repeat the process to achieve optimization.
To ensure the machine learning computer operates efficiently, human programmers maintain the model, tweaking it if necessary. In some cases, programmers will alter parameters to keep it on track and moving toward producing accurate results.
Real-World Applications
We’ve established the fact that machine learning systems work behind the scenes to adapt and evolve without the need of explicit human input. But, where is this type of technology prevalent in our everyday lives? Some examples of real-world applications include:
1. Speech Recognition
One of the wonders of machine learning is its capability to translate speech into text. In fact, you’ve most likely interacted with them this very day! (“Siri, please take me to the OutstandingStar home page.”) There are several common software applications and devices that take your voice commands and download them in the form of a text file by recording your speech and pinpointing voice inflections, volume, and cadence. Some of the most common examples include virtual assistants, speech-to-text applications, in-car roadside assistance, and automated customer service messages.
2. Image Recognition
Pixels are the tiny, individual elements of a picture that make up an entire image. Machine learning can recognize an image — with or without color — based on an image’s pixel makeup. If you’ve ever used apps that accurately adapt color to a black and white image or altered images with stylized photo filters, then you’ve seen machine learning at work! Pretty cool, huh?
3. Medical Diagnosis
Where many don’t realize machine learning is on display is in the health care and medical devices field. Doctors and physicians often use chatbots or other voice recognition tools to detect symptom patterns a patient may be experiencing. Image recognition and imaging machines also play a key role in enhancing and innovating these fields. Additionally, Lean Six Sigma methodologies are used to recognize defects in medical devices.
4. Statistical Arbitrage
Finance is another industry that uses machine learning. With many security and trading firms sifting through thousands of transactions per day, it’s common for them to use what’s called “arbitrage,” which is an automated trading approach. It utilizes a complex trading algorithm that examines different possibilities, trends, and similarities to make sound decisions.
Machine learning can explore the inner workings of a given financial market and its framework, follow potential at-risk models and decide how to interact, and identify the reasons for a failed trade settlement.
5. Predictive Analytics
By now you’ve learned that machine learning is all about rules, rules, and more rules. When data is gathered, tech professionals can segment the data that follows the previously established rules. Now that each piece of data is categorized, an analyst can find trends and possibilities that could lead to a potential failure. Using predictive analytics, a business can gauge its operational performance and make necessary changes to improve future outcomes.
In this field, machine learning is especially important when forecasting sales quotes by using historical seasonal buying trends, predicting real estate pricing, and determining the authenticity of a business settlement.
6. Self-Driving Cars
Machine learning allows autonomous cars to recognize objects, understand environments, and make decisions based on object recognition and object classification algorithms.
There are three types of machine learning algorithms methods. Let’s break them down:
Machine Learning Methods
1. Supervised Learning
Supervised machine learning uses labeled datasets to train algorithms and allows computers to predict outcomes accurately. Beginning with the analysis of a known training dataset, the input data is implemented into the model. After implementation, certain coded segments are assigned a weight score to indicate how important the segment is before testing the data again.
As with anything, training makes a thing grow in efficiency and perform better overall. Programmers will continuously train a model which allows it to act accordingly when a new input is implemented.
2. Unsupervised Learning
Unsupervised machine learning uses pattern recognition to draw inferences from datasets with input data that is unlabeled and unclassified.
As the algorithms adapt to new situations, they’re able to pick out data patterns independently and alter their way of “thinking” — without human interference. This is an ideal outcome for a business that is attempting to expand its reach to new markets, demographics, and consumer trends.
3. Semi-supervised Learning
With semi-supervised machine learning, you’ll find a middle ground between supervised and unsupervised as it uses both labeled and unlabeled data.
In the case of a semi-supervised learning system, you’ll see they have the ability to determine faults and find solutions without having the amount of labeled data typically required for a supervised learning algorithm.
Reinforcement Machine Learning
Reinforcement machine learning is a performance-based method that trains through trial and error. System actions are generated as it interacts with its environment. As it operates, it will uncover present errors while also pinpointing different rewards. By determining the optimal behavior, machines and software agents can maximize their performance. Throughout the process, the system will need what’s called a reinforcement signal, which is the feedback that helps the system develop optimal tendencies.
Why It’s Important
The machine learning field is ever-evolving, and with evolutions come a rise in demand and significance. Machine learning offers high-value predictions that can guide ideal decisions and intelligent actions in real-time void of human intervention. Because it analyzes such large chunks of data, data scientists have an easier time delivering more complex data and faster, more accurate results. By involving automatic sets of generic methods that have replaced traditional statistical techniques, machine learning has changed how data is extracted and analyzed.
The Coding Factor
If you’re dead set on pursuing a career in AI or machine learning, a little coding knowledge goes a long way. Machine learning is applied through coding. Programmers who understand how to implement the necessary code have a strong grasp on how the algorithms operate and will be able to maintain and optimize algorithms along the way.
To make a long story short, learning to code is the gateway to becoming a machine learning professional.And one of the most common languages used in machine learning is Java.
Get Started
Machine learning continues to change our way of life one day at a time. Code is the foundational piece that makes all of artificial intelligence grow more and more impressive. What’s your plan?
