Machine learning is defined as an application of an artificial intelligence (AI) that provides the system the ability in order to automatically learn and improve from experience without being explicitly programmed. Machine learning mainly focuses on the development of computer programs that could access the data and use it to learn for themselves.
The process of machine learning begins with the observations or user data, such as examples includes direct experience, or instructions, to look for the patterns in the data and able to make good decisions in the future based on examples that we provide to the machine. The primary aim is to allow the computers systems in order to learn it automatically without the human intervention or even the assistance and adjust actions accordingly.
Machine learning algorithms are often categorized as supervised or unsupervised.
Supervised machine learning algorithms can be applied what has learned in the past to the new data using labeled examples to predict the future events. Starting from analysis of a known training dataset, the machine learning algorithm can also produce and conclude function to make predictions about the output values. The system could also able to provide targets for any new input after sufficient training. The machine learning algorithm could also compare its output with the correct, intended output and also able to find errors in order to modify the model accordingly.
In contrast to that, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised machine learning studies how the systems can infer the function to describe a hidden structure from un-labeled data. The system not able figure out the right output, but it also explores the data and can draw inferences from datasets in order to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere between supervised and unsupervised learning, since they are used in both labeled and unlabeled data for training typically a small amount of labeled data and a large amount of un-labeled data. The systems that is used in this method are able to considerably improve machine learning accuracy. Usually, the semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it or learn from it. Otherwise, for acquiring un-labeled data generally does not require additional resources.
Reinforcement machine learning algorithms is a continuous learning method that interacts with its environment by producing actions and discovers errors. Trial and error search and delayed reward are most relevant characteristics of reinforcement learning. This method allows machines and software systems to automatically determine the ideal behaviour within a specific context in order to maximize its overall performance. Simple reward of feedback is required for the system to learn which action is best and this is known as the reinforcement signal.
Machine learning enables the analysis of massive quantity of data. While it generally delivers faster, more accurate results in order to identify the profitable opportunities or dangerous risks, it may also required the additional time and resources to train the system properly. Combining machine learning with Artificial Intelligence and cognitive technologies could make it even more effective in processing large volumes of information.
Top 5 Machine Learning Applications
Siri, Alexa, Google are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding relevant information, when we asked over voice. All you need to do is to activate them and say “What is the weather today?”, “What are the flights from New York to California”, etc. For answering, your personal assistant looks out for the information by recalling your related queries, or also send a command to other resources to collect info. You can even instruct the assistants for certain tasks like “Set a reminder for 10 AM next morning”, “Remind me to visit Passport Office day after tomorrow”.
Machine learning is very important part of these personal assistants as they collects and refine the information on the basis of our previous involvement with them. Later, this set of data is then utilized to render the results that are tailored to your preferences.
Virtual Assistants are usually integrated to a variety of platforms. For example:
Smart Speakers: Google Home & Amazon Echo
Smartphones: iPhone 11 or Samsung S8
Mobile Apps: Google Allo
Traffic Predictions: We all have been already using GPS navigation services. While we do that, our current locations and speed are being saved at a central server in order to manage the traffic. This data is then been used to build a map of the current traffic. While this also helps in preventing the traffic and also does congestion analysis, the underlying problem is that there are very less number of cars that are equipped with the GPS. Machine learning in such scenarios helps to estimate the regions where the congestion can be found on the basis of our daily experiences.
Imagine a person is monitoring multiple video cameras! Certainly, it is a difficult job to do and also boring as well. This is why the idea of training the computers to do this jobs makes sense.
The video surveillance system nowadays are driven by Artificial Intelligence that makes it possible to detect the crime before they happen. They can even track the unusual behaviour of people like standing motionless for long time, napping or stumbling, on benches etc. The system can thus give an alert to the human attendants, which can even ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they may help to improve the surveillance services. This happens with machine learning doing its job at the backend.
From personalizing your social media news feeds to better ads targeting, social media platforms are now utilizing the machine learning for their own and the user benefits. Here are some examples that you must be noticing and loving your social media accounts, without even realizing that these wonderful features are nothing but the applications of Machine Learning.
Face Recognition: You may upload a picture of you with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, it notices the unique features, and then also matches them with the people in your friends list. The entire process at the software's backend is very complicated and aslo takes care of the precision factor but seems to be very simple application of Machine Learning at the front end.
There are a various number of spam filtering approaches that the email clients use. In order to ascertain that these spam filters are regularly updated, they are driven by machine learning. When the rule-based spam filtering is usually done, it also fails to track the latest tricks adopted by spammers. Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the examples of spam filtering techniques that are powered by Machine Learning.
Over more than 325, 000 malware are detected on everyday basis and each piece of code is almost 98% similar to its previous versions. The system security programs that are driven by machine learning understand the programming pattern. Therefore, they also detects new malware with 1-10% variation very easily and offer the protection against them.