What is Machine Leaning? And why it is important!
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Evalouation of Machine Learning:
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 specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
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 Twitter? Machine learning combined with linguistic rule creation.
- Fraud detection? One of the more obvious, important uses in our world today.
- Youtube search suggetions according to your intreast ! are examples of machine learning.
- Machine Learning and Artificial Intelligence
- While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.
Who's using it?
Most industries working with large amounts of data have
recognized the value of machine learning technology. By gleaning insights from
this data – often in real time – organizations are able to work more
efficiently or gain an advantage over competitors.
Financial services
Banks and other businesses in the financial
industry use machine learning technology for two key purposes: to identify
important insights in data, and prevent fraud. The insights can identify
investment opportunities, or help investors know when to trade. Data mining can
also identify clients with high-risk profiles, or use cybersurveillance to
pinpoint warning signs of fraud.
Government
Government agencies such as public safety and utilities have a
particular need for machine learning since they have multiple sources of data
that can be mined for insights. Analyzing sensor data, for example, identifies
ways to increase efficiency and save money. Machine learning can also help
detect fraud and
minimize identity theft.
Health care
Machine learning is a fast-growing trend in the
health care industry, thanks to the advent of wearable devices and sensors that
can use data to assess 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.
Retail
Websites recommending items you might like based on previous
purchases are using machine learning to analyze your buying history.
Retailers rely on machine learning to capture data, analyze it and use it to
personalize a shopping experience, implement a marketing
campaign, price
optimization, merchandise supply planning,
and for customer insights.
Oil and gas
Finding new energy sources. Analyzing minerals
in the ground. Predicting refinery sensor failure. 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 expanding.
Transportation
Analyzing data to identify patterns and trends is key to the
transportation industry, which relies on making routes 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.
What are some popular machine learning methods?
Two of the most widely adopted machine
learning methods are supervised learning and unsupervised learning – but there are also other methods of machine learning. Here's an
overview of the most popular types.
Supervised learning algorithms are trained using labeled
examples, such as an input where the desired output is known. For example, a
piece of equipment could have data points labeled either “F” (failed) or “R”
(runs). The learning algorithm receives a set of inputs along with the
corresponding correct outputs, and the algorithm learns by comparing its actual
output with correct outputs to find errors. It then modifies the model
accordingly. Through methods like classification, regression, prediction and
gradient boosting, supervised learning uses patterns to predict the values of
the label on additional unlabeled data. Supervised learning is commonly used in
applications where historical data predicts likely future events. For example,
it can anticipate when credit card transactions are likely to be fraudulent or
which insurance customer is likely to file a claim.
Unsupervised learning is used against data that has no historical labels. The system is
not told the "right answer." The algorithm must figure out what is
being shown. The goal is to explore the data and find some structure within.
Unsupervised learning works well on transactional data. For example, it can
identify segments of customers with similar attributes who can then be treated
similarly in marketing campaigns. Or it can find the main attributes that
separate customer segments from each other. Popular techniques include
self-organizing maps, nearest-neighbor mapping, k-means clustering and singular
value decomposition. These algorithms are also used to segment text topics,
recommend items and identify data outliers.
Semisupervised learning is used for the same applications as
supervised learning. But it uses both labeled and unlabeled data for training –
typically a small amount of labeled data with a large amount of unlabeled data
(because unlabeled data is less expensive and takes less effort to acquire).
This type of learning can be used with methods such as classification,
regression and prediction. Semisupervised learning is useful when the cost
associated with labeling is too high to allow for a fully labeled training
process. Early examples of this include identifying a person's face on a web
cam.
Reinforcement learning is often used for robotics, gaming and navigation. With
reinforcement learning, the algorithm discovers through trial and error which
actions yield the greatest rewards. This type of learning has three primary
components: the agent (the learner or decision maker), the environment
(everything the agent interacts with) and actions (what the agent can do). The
objective is for the agent to choose actions that maximize the expected reward
over a given amount of time. The agent will reach the goal much faster by
following a good policy. So the goal in reinforcement learning is to learn the
best policy.
If you want to learn about the term Arificial Intelligence than read the full article now!
