Machine learning is the science of getting computers to act without being specifically programmed. In the past years, machine learning has given us self-driving cars, practical speech recognition, efficient web search, and a considerably improved understanding of the human genome. Machine learning is so pervasive today that you apparently use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform particular tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative (Relating to or involving iteration, especially of a mathematical or computational process.) perspective of machine learning is important because as models are disclosed to new data, they are able to separately adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a technology which is not new – but on another level.
While many machine learning algorithms have been in use for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:
- The heavily hyped, self-driving Google car? The essence of machine learning.
- Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
- Knowing what customers are saying about you on social media? Machine learning combined with grammatical rule creation.
- Fraud detection? One of the more obvious, important uses in our world today.
Importance of machine learning:
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian (based on Bayes‘ theorem) analysis more popular than ever. Things like growing volumes and variations of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an association has a better chance of identifying profitable opportunities or avoiding unknown hazards.
Most corporations working with large amounts of data have recognized the value of machine learning technology. By gathering insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over their rivals.
Banks and other businesses in the financial sectors use machine learning technology for two key purposes: to identify important penetrations in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify customers with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.
Government agencies such as public safety and services have a particular need for machine learning since they have multiple sources of data that can be dug for insights. Analyzing sensor data, for example, identifies ways to increase productivity and save money. Machine learning can also help detect fraud and reduce identity theft.
Marketing and sales
Websites suggesting products you might like based on previous purchases are using machine learning to analyze your buying history – and promote other items you’d be interested in. This ability to obtain data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail.
Examining data to identify patterns and trends is key to the transportation industry, which relies on making roads more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation, and other transportation organizations.
Oil and gas
Finding new energy source site. Analyzing minerals in the earth. Predicting refinery sensor malfunction. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still growing
Machine learning is a fast-growing trend in the healthcare industry, thanks to the approach of wearable devices and sensors that can use data to evaluate a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
Machine learning uses two different types of techniques: supervised learning, which trains a model on known input and output data so that it can foretell future outputs, and unsupervised learning, which finds hidden patterns or inherent structures in input data.
Supervised machine learning builds a model that makes predictions based on evidence in the presence of ambiguity. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and prepares a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict.
Supervised learning uses classification and regression techniques to develop predictive models.
Classification techniques predict discrete responses—for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.
Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.
Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks.
Regression techniques predict continuous responses—for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.
Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment.
Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.
Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.
Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for cluster analysis incorporate gene sequence analysis, market research, and object recognition.
For example, if a cell phone company wants to optimize the locations where they build cell phone towers, they can use machine learning to predict the number of groups of people relying on their towers. A phone can only talk to one tower at a time, so the team uses clustering algorithms to design the best installation of cell towers to optimize signal reception for groups, or clusters, of their customers.
Common algorithms for performing clustering include k-means and k-medoids, hierarchical clustering, Gaussian mixture models, hidden Markov models, self-organizing maps, fuzzy c-means clustering, and subtractive clustering.
Machine Learning Applications:
1.Creating Algorithms that Can Analyze Works of Art
Researchers at the Art and Artificial Intelligence Laboratory at Rutgers University wanted to see whether a computer algorithm could classify paintings by style, genre, and artist as easily as a human. They began by identifying visual features for classifying a painting’s style. The algorithms they developed listed the styles of paintings in the database with 60% accuracy, outperforming typical non-expert humans.
The researchers hypothesized that visual features useful for style classification (a supervised learning problem) could also be used to determine artistic influences (an unsupervised problem).
They used classification algorithms trained on Google images to identify specific objects. They tested the algorithms on more than 1,700 paintings from 66 different artists working over a span of 550 years. The algorithm readily identified connected works, including the influence of Diego Velazquez’s “Portrait of Pope Innocent X” on Francis Bacon’s “Study After Velazquez’s Portrait of Pope Innocent X.”
2. Optimizing HVAC Energy Usage in Large Buildings
The heating, ventilation, and air-conditioning (HVAC) systems in office buildings, hospitals, and other large-scale commercial buildings are usually inefficient because they do not take into account changing weather patterns, variable energy costs, or the building’s thermal properties.
BuildingIQ’s cloud-based software platform discusses this problem. The platform uses exceptional algorithms and machine learning methods to continuously process gigabytes of information from power meters, thermometers, and HVAC pressure sensors, as well as weather and energy cost. In particular, machine learning is used to segment data and define the relative contributions of gas, electric, steam, and solar power to heating and cooling processes. The BuildingIQ platform reduces HVAC energy consumption in large-scale commercial buildings by 10–25% during normal operation.
3. Virtual personal assistants.
Alexa, Siri, Google Now are some of the most popular examples of virtual personal assistants. As the name suggests, they help in finding information, when asked over voice. All you need to do is initiate them and ask “What is my schedule for tomorrow?”, “What are the flights from India to Japan”, or similar questions. For answering, your personal assistant watches out for the information, summons your related questions, or send a request to other resources (other apps) to collect information. You can even command assistants for certain tasks like “Set an alarm for 5 AM next morning”, “Remind me to visit a Bank day after tomorrow”.
Machine learning is an essential part of these personal assistants as they collect and improve the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.