What is Machine Learning?
According to the SAS Institute, Inc., machine learning is the “method of data analysis that automates analytical model building.” It is a subset of artificial intelligence (AI) that focuses on the idea that systems can understand and interpret data and identify patterns and structures to enable learning, reasoning, and decision making with minimal human intervention. In other words, machine learning allows users, like us, to feed a computer algorithm with an immense number of data and have it analyze, interpret, and make data-recommended advice and decisions. If the algorithm identifies any corrections, it can incorporate that new information to improve its decision-making process in the future.
A machine learning algorithm also referred to as a model, is a mathematical expression that represents data in the context of a problem, especially in business. It aims to go from data to insight or feedback. For instance, if an online retailer wants to anticipate and predict sales for the next quarter, they might want to use a machine-learning algorithm that predicts those sales based on past sales data and other relevant information.
To demystify machine learning and to offer a learning path for those who are new to the core concepts, let’s look at a few different methods, including simple descriptions and examples for each one.
A few machine learning methods:
Often machine learning systems are categorized as supervised or unsupervised.
- Supervised machine learning algorithms can utilize the information that has been learned in the past to new data using labeled examples to predict future conditions and events. By analyzing a known training dataset, the learning algorithm comes up with an inferred function to make predictions about the output values. The system can provide targets with new insight and information after sufficient training. This learning algorithm can also compare its output with the exact, intended output and find flaws to modify the model accordingly.
- On the other hand, unsupervised machine learning algorithms are helpful when the information used in training the new system is neither classified nor labeled. Unsupervised learning analyzes and examines how systems can infer a function to illustrate a hidden structure from unlabeled data. The system does not figure out the right output, but it tests and experiments with the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
- There may also be semi-supervised machine learning algorithms, which fall somewhere between supervised and unsupervised learning since they use both labeled and unlabeled data for the new system’s training — this typically has a relatively smaller amount of labeled data and an immense amount of unlabeled data.
The systems that make use of this method can profoundly improve learning accuracy. Semi-supervised learning is useful when the labeled data on hand requires skilled and relevant resources to train the algorithm. Otherwise, acquiring unlabeled data does not necessarily require additional resources.
- Reinforcement machine learning algorithms are a learning method that interacts and communicates with its environment by producing actions/motives and discovers errors or rewards. Trial and error searches and delayed rewards are the most relevant aspects of reinforcement learning.
This method gives machines and software the necessary agents to automatically determine and classify the behavior and action within a specific context to maximize its performance. It requires simple reward insight for the agent to learn and understand which action to take; the action taken is the reinforcement signal.
Machine learning enables the analysis of massive quantities of data in real-time, with better accuracy. While it typically provides faster and more accurate results to identify profitable opportunities or hazardous risks, it may also need more time and resources to train the algorithm. A combination of machine learning with AI and cognitive technologies can make a huge difference in making more effective and reliable processing systems for large volumes of information.
How does machine learning work?
Machine learning is composed of three major elements:
- The computational algorithm, which is the core of the operation that is used to make solid resolutions appropriate to a situation
- The variables and various features that make up the decision
- Base knowledge, for which the answer is known, that informs and enables (trains) the system to learn
To test an algorithm’s functionality and speed, the model needs initial parameter data (test data) to put the algorithm into testing. The algorithm’s output (learning) will then need appropriate adjustments until it reconciles with the known answer. Here, the increasing amount of data is what helps the system learn and process higher computational decisions at higher speeds and higher quality.
So, why is machine learning important?
Since time began, data has always been the lifeblood of any ambitious business. These data are what market researchers use to adjust their products to the specific needs and wants of their customers/market, making it more likely for people to purchase or acquire the company’s services. The ability to make data-driven decisions results in a massive difference between keeping up with the competition in your business and falling further behind.
This resurging interest in machine learning is caused by the same elements that have made data mining, the Bayesian analysis, more famous than ever before. Instances like growing volumes and variations in available information and date produce more accurate computational processing and more capable, a lot cheaper, and more affordable data storage.
These things conclude the possibility of quickly and automatically producing models that can analyze bigger, more difficult data and deliver it to the user faster and with more accurate results. By building more reliable models, an organization/company has a better chance of classifying profitable opportunities or avoiding unwanted risks. However, an algorithm may still crash, causing harmful consequences.
What is required to make a great machine learning model/system?
Like every machine, there are certain things that an operator needs to acquire for his system to run smoothly and with little complications. Here are some of the requirements needed in making a machine learning system:
- Data preparation capabilities
- Algorithms – basic and advanced
- Automation and iterative processes
- Ensemble modeling
Who can benefit from machine learning systems?
The majority of large industries that work with immense amounts of data have come to know the value of machine learning technology. They put aside budgets and funds for the development of a reliable system and employ professionals who can run and repair it. By gleaning information and insight from the data gathered (often in real-time), organizations can work more efficiently or gain an advantage over competitors.
Banks and other financial institutions use machine learning technology for two major reasons: to classify important observations from data and prevent fraud. Machine learning technology helps identify opportunities for investment and help investors predict the perfect time to trade. Data mining can also determine high-risk clients or use cyber surveillance to identify warning signs of misrepresentations and fraud.
Government sectors that ensure public safety and utilities have a specific need for machine learning since they have a huge magnitude of data that might store important insights. Evaluating and examining sensor data, for example, can help identify ways to increase work efficiency and save money. Machine learning can detect and minimize identity theft, as well.
Machine learning is also a fast-developing trend in the healthcare industry due to the advent of wearable devices and sensors that utilize data and information to assess a patient’s health and vitals in real-time. This technology can also help medical experts analyze and interpret data to classify trends or red flags that may lead to improved diagnoses and treatment administrations.
Websites that recommend items that you might fancy based on your previous searches and purchases are also using machine learning systems to analyze your buying history. This is why a large number of retailers rely on machine learning to capture needed data, analyze it and apply it to personalize a unique shopping experience for a specific user, implement a newly-structured and integrated marketing campaign, optimize product and service prices, update merchandise supply planning, and gather customer insight and feedback.
Even the transportation industry uses machine learning to analyze data to identify patterns and trends, which makes it possible for making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling factors that machine learning contains are important tools for delivery companies, public transportation, and other transportation organizations.
What are the downsides of machine learning?
First off, you need a lot of computers and computer technicians to power your system and make it running. The complex nature of algorithms makes it prone to crashing, and immediate intervention and repair are needed to prevent mass loss of information and data. This is why companies set aside large amounts of money for the advancement and maintenance of their machine learning systems.
A machine learning system’s unpredictable nature also makes it quite a hassle to maintain. It can look as if something is wrong with its functionality when there is nothing to worry. Technicians are often alerted when their attention is not warranted, therefore becoming a huge time-consuming expense.
Despite its lapses, machine learning delivers a huge amount of benefits that outweigh all the bad.
The Differences Between These Three Methods: Data Mining, Machine Learning, and Deep Learning
We’ve mentioned all three of these concepts previously. But what are they really, and how do they correspond with one another? All three methods share a common goal (to extract insights, patterns, and relationships that can be used to make decisions); however, they have different approaches to achieve that goal.
- Data Mining
This method can be considered a superset of several different approaches to extracting insight and feedback from gathered data. It may or may not involve traditional quantitative methodologies and machine learning systems. Data mining employs methods taken from different areas to classify and identify previously anonymous patterns from data, including numerical algorithms, text and content analytics, time series analysis, and other aspects of analytics. Data mining can also include the study and practice of data storage and data manipulation.
- Machine Learning
Like many statistical models, machine learning understands the structure of the data given, including fitting theoretical distributions to the data to make it well-understood.
With statistical models, there is a revolving theory supported by mathematical information that requires the data to meet strong assumptions.
Machine learning has progressed based on the ability to use computers in probing the data for structure, even if we do not have a theory of what that structure can look like. The machine learning model runs through an analysis stage that is a validation error on new data, not a theoretical test that proves a null hypothesis.
Because machine learning often uses an iterative approach to learn from data, learning becomes easily automated. The ones that pass run through the data until a potent and plausible pattern shows up.
- Deep learning
Deep learning combines advances in computing power and special types of neural networks to identify and familiarize itself with complicated patterns in large amounts of data. State-of-the-art deep learning techniques are useful for identifying objects in distorted images and words in sounds. Researchers from various studies are currently looking for ways to apply these successes in pattern recognition to more complex conditions and situations, such as automatic language translation, medical diagnoses, and many other important social and business problems.