Machine learning (ML) equips computers to learn and interpret without being explicitly programmed to do so. Here, as the "computers", also referred as the "models", are exposed to sets of new data, they adapt independently and learn from earlier computations to interpret available data and identify hidden patterns. This involves data analysis and automation of analytical model-building using numerous ML algorithms. ML enables computers and computing machines to search for and identify hidden insights, without being programmed for where to look for, when exposed to new data sets.
Although this technology is not new, it is now gaining fresh momentum as there are numerous things to know about ML. The factors responsible for resurging interest in ML are powerful and affordable computational processing, continuously growing volumes of huge data sets, and affordable data storage options. Today, companies can make informed decisions by using ML algorithms to develop analytical models, which uncover connections, trends and patterns with minimal or no human intervention.
Today, machine learning is different from what it used to be in the past, due to the emergence of advanced computing technologies. Initially, it had gained momentum due to pattern recognition and the fact that computers did not have to be programed to execute certain tasks to learn. Many researchers who were interested in Artificial Intelligence (AI) investigated this area further to find out whether computers could really learn from data or not.
The focus here is on iterative learning. Machines begin to adapt to new data that they are exposed to, over a period. Based on the patterns and computations that are previously created, machines learn to repeat decisions made in the past, in similar situations. This aspect of machines' ability to learn from the existing patterns, is now gaining huge momentum.
Today, people are sitting up and taking notice of the fact that machines are now able to apply complicated mathematical calculations to areas, such as big data, at a much faster rate. Consider Google Car for instance, which is primarily built on the crux of machine learning. Another important use of machine learning can be found in regular recommendations that are rolled out by companies like Netflix and Amazon - an example of machine learning in everyday life. Next, ML can also be combined with linguistic rules creation. This application is implemented by Twitter, where you will know what customers say about you. And not to forget, machine learning is significantly being used to detect fraud in various industry sectors.
Gone are the days when programmers would tell a machine how to solve a problem at hand. We are in the era of machine learning where machines are left to solve problems, on their own, by identifying the patterns in each data set. Analyzing hidden trends and patterns makes it easy to predict future problems and prevent them from occurring.
A machine learning algorithm usually follows a certain type of data and then uses the patterns hidden in that data to answer more questions. For example showing a computer a series of photographs, some of which say that "this is a horse" and some of which say "this is not a horse." After this exercise, if you show some more photographs to the same computer, it will be on a mission to identify which of those photographs are of a horse and which of those are not that of a horse. Every correct and incorrect guess of the computer is added to its memory, which makes it smarter in the longer run and enriches its learning over a period.
To get the maximum value from big data, businesses must know exactly how to pair the right algorithm with a particular tool or process and build machine learning models based on iterative learning processes. Some of the key machine learning algorithms are -
As mentioned above, the secret to successfully harnessing the applications of ML lies in not just knowing the algorithms, but in pairing them accurately with the right tools and processes, which include -
Most of the industries dealing with huge amounts of data have now recognized the value of machine learning. By gleaning hidden insights from this data, businesses can work more efficiently and can also gain a competitive edge. Besides, affordable and easy computational processing and cost-effective data storage options have made it feasible to develop models that quickly and accurately analyze huge chunks of complex data. Apart from enabling enterprises to identify trends and patters from diverse data sets, ML also enables businesses to automate analysis, which was traditionally done by humans. Using ML organizations can deliver personalized services and differentiated products that precisely cater to varying needs of the customers. Additionally, ML also helps companies to identify opportunities that can be profitable in the long run.
If you are planning to develop effective machine learning systems for augmenting your business, then here is what it takes -
The value of machine learning technology has been recognized by companies across several industries that deal with huge volumes of data. By leveraging insights obtained from this data, companies are able work in an efficient manner to control costs as well as get an edge over their competitors. This is how some sectors / domains are implementing machine learning -
Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud, with the help of machine learning technology. The technology is also used to identify opportunities for investments and trade. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take necessary actions in time to prevent fraud.
Companies are using machine learning technology to analyze the purchase history of their customers and make personalized product recommendations for their next purchase. This ability to capture, analyze, and use customer data to provide a personalized shopping experience is the future of sales and marketing.
Government agencies like utilities and public safety have a specific need FOR Ml, as they have multiple data sources, which can be mined for identifying useful patterns and insights. For example sensor data can be analyzed to identify ways to minimize costs and increase efficiency. Furthermore, ML can also be used to minimize identity thefts and detect fraud.
With the advent of wearable sensors and devices that use data to access health of a patient in real time, ML is becoming a fast-growing trend in healthcare. Sensors in wearable provide real-time patient information, such as overall health condition, heartbeat, blood pressure and other vital parameters. Doctors and medical experts can use this information to analyze the health condition of an individual, draw a pattern from the patient history, and predict the occurrence of any ailments in the future. The technology also empowers medical experts to analyze data to identify trends that facilitate better diagnoses and treatment.
Based on the travel history and pattern of traveling across various routes, machine learning can help transportation companies predict potential problems that could arise on certain routes, and accordingly advise their customers to opt for a different route. Transportation firms and delivery organizations are increasingly using machine learning technology to carry out data analysis and data modeling to make informed decisions and help their customers make smart decisions when they travel.
This is perhaps the industry that needs the application of machine learning the most. Right from analyzing underground minerals and finding new energy sources to streaming oil distribution, ML applications for this industry are vast and are still expanding.
Although supervised and unsupervised learning are two of the most widely accepted machine learning methods by businesses today, there are various other machine learning techniques. Following is an overview of some of the most accepted ML methods -
These algorithms are trained using labeled examples, in different scenarios, as an input where the desired outcome is already known. An equipment, for instance, could have data points such as "F" and "R" where "F" represents "failed" and "R" represents "runs".
A learning algorithm will receive a set of input instructions along with the corresponding accurate outcomes. The learning algorithm will then compare the actual outcome with the accurate outcome and flag an error, if there is any discrepancy. Using different methods, such as regression, classification, gradient boosting, and prediction, supervised learning uses different patterns to proactively predict the values of a label on extra unlabeled data. This method is commonly used in areas where historical data is used to predict events that are likely to occur in the future. For instance, anticipate when a credit card transaction is likely to be fraudulent or predict which insurance customers are likely to file their claims.
This method of ML finds its application in areas were data has no historical labels. Here, the system will not be provided with the "right answer" and the algorithm should identify what is being shown. The main aim here is to analyze the data and identify a pattern and structure within the available data set. Transactional data serves as a good source of data set for unsupervised learning.
For instance, this type of learning identifies customer segments with similar attributes and then lets the business to treat them similarly in marketing campaigns. Similarly, it can also identify attributes that differentiate customer segments from one another. Either ways, it is about identifying a similar structure in the available data set. Besides, these algorithms can also identify outliers in the available data sets.
Some of the widely used techniques of unsupervised learning are -
This kind of learning is used and applied to the same kind of scenarios where supervised learning is applicable. However, one must note that this technique uses both unlabeled and labeled data for training. Ideally, a small set of labeled data, along with a large volume of unlabeled data is used, as it takes less time, money and efforts to acquire unlabeled data. This type of machine learning is often used with methods, such as regression, classification and prediction. Companies that usually find it challenging to meet the high costs associated with labeled training process opt for semi-supervised learning.
This is mainly used in navigation, robotics and gaming. Actions that yield the best rewards are identified by algorithms that use trial and error methods. There are three major components in reinforcement learning, namely, the agent, the actions and the environment. The agent in this case is the decision maker, the actions are what an agent does, and the environment is anything that an agent interacts with. The main aim in this kind of learning is to select the actions that maximize the reward, within a specified time. By following a good policy, the agent can achieve the goal faster.
Hence, the primary idea of reinforcement learning is to identify the best policy or the method that helps businesses in achieving the goals faster. While humans can create a few good models in a week, machine learning is capable of developing thousands of such models in a week.
While all of these methodologies have one single goal of deriving insights, patterns and trends to make more informed decisions, all of them have different approaches to the same. Let's look at some of them below.
This process is a superset of numerous methods, which might involve machine learning and traditional statistical methods, to derive useful insights from the available data. It is primarily used to discover those patterns in a data set, which were not known previously. This approach includes machine learning, statistical algorithms, time series analysis, text analytics, and other domains of analytics. Besides, data mining also involves the study and practice of data manipulation and data storage.
Like various statistical models available in the market, the main aim of machine learning is to determine and properly understand the structure and patterns hidden in data. Then, theoretical distributions are applied to data sets to gain a better understanding. Every statistical model is backed by mathematically proven theories. However, machine learning is hugely based on the ability of the computers to dig deeper into the available data to unleash a structure, even in the absence of a theory on what the data structure could look like.
Machine learning models are tested using a validation error on new data sets, contrary to going through a theoretical test that confirms a null hypothesis. As machine learning is iterative in nature, in terms of learning from data, the learning process can be automated easily, and the data is analyzed until a clear pattern is identified.
With the ability to combine computing power and unique neural networks to learn complex patterns in huge volumes of data, deep learning techniques are used to identify words within sounds, and objects within images. Several researchers intend to replicate this success achieved in recognizing patterns to address more complicated tasks like medical diagnosis, business problems, language translation, and other social problems.
Being a top innovative trend, machine learning is now being implemented by several businesses across the globe. With machine learning applications gaining prominence in most of the industries, are you all set to implement this technique in your business?
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