What is Machine Learning?
Machine learning is an application of artificial intelligence. It gives the ability to systems to learn and improve itself automatically from experience without being explicitly programmed. The main focus is given on the development of computer programs that can access data and use it to learn for themselves.
Machine learning is the capability of the software to perform task or a series of tasks smartly and intelligently without being programmed for those activities. It is a part of AI. In general the software behaves in the same way the programmer has programmed it. But, in the case of machine learning, it is going one step further by making the software capable of accomplishing intended tasks by using statistical analysis and predictive analytics techniques. So, we can say that Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn with data, without being explicitly programmed.
It is closely related to computational statistics, which also focuses on prediction-making through the use of computers. It has strong relation with mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes relate to data mining, where the subsequent subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” — Tom Mitchell, Carnegie Mellon University
Machine Learning can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization.
The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly.
How Machine Learning Works?
Machine Learning is a technique of AI that allows a system to work intelligently by using some complex algorithms and set of predefined rules. It uses the past data to read the patterns. On the basis of data analysis it generates the relevant data or performs the intended task on the basis of defined rules and algorithms.
Types of Machine Learning
We can categorized machine learning into 3 main categories: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Supervised Learning
In Supervised Learning, We may not know the inner relations of the data we are processing, but we know very well which is the output that we need from our model.
In supervised learning, we have a full set of labeled data while training an algorithm. Full set of labeled data means each example in the training dataset is also carrying the answer and the algorithm should come up with on its own. So, a labeled dataset of flower images would tell the model which photos were of roses, daisies and daffodils. When shown a new image, the model compares it to the training examples to predict the correct label.
We can apply supervised learning in expert systems for image recognition, speech recognition, forecasting, and also in some specific business domain like Targeting, Financial analysis etc.
Supervised learning is, thus, best suited to problems where there is a set of available reference points or a ground truth with which to train the algorithm. But those aren’t always available.
Unsupervised Learning
Unsupervised learning does not use output data (at least output data that are different from the input). Most of the time unsupervised learning algorithms are used to pre-process the data, during the exploratory analysis or to pre-train supervised learning algorithms.
In unsupervised learning, a deep learning model is handed a dataset without explicit instructions on what to do with it. The training dataset is a collection of examples without a specific desired outcome or correct answer. The neural network then attempts to automatically find structure in the data by extracting useful features and analyzing its structure.
Reinforcement Learning
Reinforcement learning algorithms try to find the best ways to earn the greatest reward. Rewards can be winning a game, earning more money or beating other opponents.
In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward.
To make its choices, the agent relies both on learnings from past feedback and exploration of new tactics that may present a larger payoff. This involves a long-term strategy — just as the best immediate move in a chess game may not help you win in the long run, the agent tries to maximize the cumulative reward.
Reinforcement learning is the field that studies the problems and techniques that try to retro-feed its model in order to improve. In order to accomplish this, RL needs to able to “sense” signals, automatically decide on an action, and then compare the outcome against a “reward” definition.
Applications of Machine Learning
As we have discussed above Machine Learning is a part of artificial intelligence, directly or indirectly we all are using it in our day to day life. Here are some common applications of machine learning:
Online fraud detection: companies use it to make cyberspace a secure place and tracking monetary frauds online.
Search Engine Result: all search engines uses it for search result refinement to give to more relevant results.
Email spam & malware filtering: spam filters gets continuously updated by it. The system security program of ML understands malware patterns and detect it.
Online customer support: most of the time executive is not there for live customer support it is usually done by chatbot that extract informative from website and present it to customer. It is done by ML.
Shopping recommendations: you usually get shopping recommendations relevant to your taste that is possible because of ML.
Social media features: some social media notifications like similar pins, people you may know, face recognition etc. are the applications of ML.
Was this article helpful? Must share your views in the comment section below. For more informative blogs, keep visiting our Tech Blogs.