What is Machine Learning? How It Works And Is Useful ?

What is Machine Learning?

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.

Machine Learning is a subset of Artificial Intelligence. Machine Learning is the study of making machines more human-like in their behavior and decisions by giving them the ability to learn and develop their own programs. This is done with minimum human intervention, i.e., no explicit programming. The learning process is automated and improved based on the experiences of the machines throughout the process. Good quality data is fed to the machines, and different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated.


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.

Why should we learn machine learning?

Machine Learning today has all the attention it needs. Machine Learning can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning. With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly create models for data analysis. Various industries depend on vast quantities of data to optimize their operations and make intelligent decisions. Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are precise and scalable and function with less turnaround time. By building such precise Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks. Image recognition, text generation, and many other use-cases are finding applications in the real world. This is increasing the scope for machine learning experts to shine as a sought after professionals.

How does it work?

Machine learning is a form of artificial intelligence (AI) that teaches computers to think in a similar way to how humans do: learning and improving upon past experiences. It works by exploring data, identifying patterns, and involves minimal human intervention. Almost any task that can be completed with a data-defined pattern or set of rules can be automated with machine learning. This allows companies to transform processes that were previously only possible for humans to perform think responding to customer service calls, bookkeeping, and reviewing resumes.

Machine Learning uses 4 main techniques :

1) Supervised Learning : allows you to collect data or produce a data output from a previous ML deployment. Supervised learning is exciting because it works in much the same way humans actually learn. In supervised tasks, we present the computer with a collection of labeled data points called a training set (for example a set of readouts from a system of train terminals and markers where they had delays in the last three months). 

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.

2) Unsupervised Learning :
helps you find all kinds of unknown patterns in data. In unsupervised learning, the algorithm tries to learn some inherent structure to the data with only unlabeled examples. Two common unsupervised learning tasks are clustering and dimensionality reduction. .

In clustering, we attempt to group data points into meaningful clusters such that elements within a given cluster are similar to each other but dissimilar to those from other clusters. Clustering is useful for tasks such as market segmentation.

Dimension reduction models reduce the number of variables in a dataset by grouping similar or correlated attributes for better interpretation (and more effective model training).

3) Semi-supervised Learning:
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.


4) Reinforcement 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.

Why Should We Learn Machine Learning?

Machine Learning today has all the attention it needs. Machine Learning can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning.

With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly create models for data analysis. Various industries depend on vast quantities of data to optimize their operations and make intelligent decisions. Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are precise and scalable and function with less turnaround time. By building such precise Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.

Image recognition, text generation, and many other use-cases are finding applications in the real world. This is increasing the scope for machine learning experts to shine as a sought after professionals. 

 

Application of Machine Learning

Financial Services:
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.

Marketing and Sales:
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:
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.

Healthcare:
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.

Transportation:
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.

Recommendation Systems:
Many businesses today use recommendation systems to effectively communicate with the users on their site. It can recommend relevant products, movies, web-series, songs, and much more. Most prominent use-cases of recommendation systems are e-commerce sites like Amazon, and many others along with  Netflix, and other web-streaming channels.