The term “Machine Learning” was first introduced in 1959 by Arthur Samuel, a pioneer in the field of computer gaming and Artificial Intelligence. Soon after the introduction of Machine learning, it becomes one of the popular areas of research obviously because of its broad scope in different fields.
In this article, you will learn in-depth about machine learning and also learn about the difference between Artificial Intelligence and Machine Learning.
Definition
Machine Learning is a field of Artificial Intelligence where computers are designed in such a way so that they can learn new data and acquire new knowledge without any human interference. The algorithms of machines can learn from new experiences and examples without being programmed explicitly by a human.
What is Machine Learning?
Machine Learning, also popularly known as ML is a scientific field of algorithms, where algorithms are designed in such a way so that they can learn from the experiences that they have been exposed to and with the help of real-world interactions. By teaching computers to become smart enough to make decisions and make choices without instructions given to them.
Computers can upgrade their knowledge based on the current data fed to it. It can analyze, observe, and self-train itself. Machine learning algorithms are designed in such a way that they can build a mathematical model using the “training data” fed to it. This mathematical model helps the machine to make predictions and also make decisions without the involvement of humans.
There are different types of machine learning algorithms, and every other day a new algorithm is introduced in the market. These algorithms basically can be classified into two broad categories such as
1. Learning style
The method used by computer algorithms to learn new knowledge or information. For example, when a computer is fed whole data manually and is supervised when it determines, it is known as supervised learning. Similarly, there is unsupervised and semi-supervised knowledge based on the learning style of computer algorithms.
2. The similarity in Form or Function
Machine learning algorithms are categorized based on their method of function, such as decision tree, regression, classification, deep learning, and clustering, etc.
Machine Learning Methods
1. Dimensionality Reduction
Dimensionality reduction machine learning method is used to remove less important information from a data set. Only the redundant columns are removed from the data set. When data is available in hundreds and thousands of columns, then it is advisable to reduce the number of columns to be able to use and modify data easily. For example, when you test a microchip within a manufacturing process.
There are chances that you apply tests on every single chip and might get redundant information. In such scenarios, it is right to use dimensionality reduction machine learning algorithms to make information manageable.
The Dimensionality reduction machine learning method is classified as an unsupervised machine learning method.
2. Regression
The regression method can be categorized under supervised machine learning. Using this method, a numerical value can be predicted or explained based on the available set of prior data. For example, the price of a property can be predicted based on the amount of the older property.
3. Clustering
The clustering machine learning method is also an example of an unsupervised machine learning method. This method is named as clustering machine learning method because the purpose of using this method is to group or cluster the observations or data with similar characteristics.
The clustering method itself defines the output and doesn’t use any output information for training purposes. In the clustering machine learning method, one can only use visualization to learn about the quality of the solution.
4. Classification
The classification is an example of supervised machine learning. This method works by predicting and explaining the class value. The classification method is used to classify the information to make a decision.
For example, online businesses can predict whether a person will buy a product online or not. The output to this query can be in the form of “Yes or No” or in the form of “buyer or No buyer.”
5. Neural Sets and Deep learning
The neural sets and deep learning are examples of nonlinear models. It captures nonlinear data by adding layers of parameters to the model. This machine learning method is opposite to the direct machine learning methods like linear and logistic regression methods.
The most straightforward neural set has one hidden layer, one input layer, and one output layer. The machine learning method becomes deep and complex, with an increased number of hidden layers in the method. This method performs best with a vast amount of data and a lot of computing power because of its self-tuning nature.
The neural sets and deep learning machine learning method is popularly used for areas of text, vision, video, and audio, etc.
6. Ensemble Methods
The ensemble machine learning method is also an example of a supervised machine learning method. This machine learning method uses several predictive models to obtain higher quality predictions than the outputs provided by one model alone.
The ensemble method uses the best of all approaches to obtain the most accurate output as all methods perform differently under different circumstances.
7. Natural Language Processing
The natural language processing method is not exactly a machine learning method. But it is a method which is used to teach information data to machines written in the form of human language. This machine learning method is essential as most of the data is written in the way of human language. Using Natural language processing (NLP) machines are taught to read the data written in human language. For example, our smartphones can autocomplete our text and correct spelling mistakes because it has been trained in natural language processing.
8. Transfer Learning
Transfer learning is a type of machine learning in which a previously trained neural set is used to perform a new but similar kind of task. A neural net can be used to perform various tasks by transferring a fraction of the trained layer into the neural set. In this way, a new neural set can be designed to perform tasks just by adding a new layer rather than developing a new neural set.
The main benefit of using transfer learning is that you don’t need a massive amount of days to train a neural set. You can prepare a neural set using a small amount of data, which is both less time consuming and economical.
9. Word Embedding
Word embedding is a machine learning method that captures the context of a word in a document.
10. Reinforcement Learning
Reinforcement learning is a type of learning method in which a machine learns by making mistakes. At the initial stage, the machine makes mistakes while trying to perform a task, and it learns from experience. In this way, it learns the right behavior from wrong behavior.
Types of Machine Learning
1. Unsupervised Learning
Unsupervised learning algorithms accept new input in the form of data and establish structure in the data such as clustering and grouping of data. Unsupervised learning algorithms learn from data that has not been labeled or classified before and provide an output based on the presence or absence or commonalities in the data set.
Unsupervised learning is also known as self-organization as it allows modeling probability densities of given inputs. One method of unsupervised learning is cluster learning; in a cluster learning subset of data is analyzed so that output for inputs for the same cluster should be the same for the same cluster and different for the different clusters based on specific criteria.
Different clustering techniques such as internal compactness, graph connectivity, and estimated density are used for making different assumptions on data of the same cluster.
2. Supervised Learning
Supervised learning is as its name suggests works in a supervised environment. In which a supervised learning algorithm designs a mathematical model. This algorithm contains both inputs as well as the desired output.
The data which is fed to the algorithm to make it learn is called “Training data.” The training data is also referred to as Supervisory Signal. Supervised learning algorithms make the use of classification and regression learning methods to learn data. The classification method is used when for input, there is a restricted set of output, whereas, regression method is used when the output of input may lie within a range of numerical numbers.
In simple words, we can say that supervised learning works using already learned Input-output pairs to find the solution for problems.
3. Reinforcement Learning
Reinforcement learning is quite different from supervised and unsupervised learning. Unlike supervised and unsupervised learning, it is difficult to understand the relationship between reinforcement learning.
Reinforcement learning can be defined as a type of machine learning that relies on a time-dependent sequence of labels. In simple words, reinforcement learning is a type of learning where learning happens by learning through mistakes. The reinforcement learning algorithm will make a lot of mistakes initially when it is applied to any environment.
To improve the working of algorithms, it is necessary to give different signals for both good and bad behavior. With time, the reinforcement machine learning algorithm will learn to make fewer mistakes than it used to make in the beginning.
The reinforcement machine learning algorithm gets influenced by fields of psychology and neuroscience. To establish a successful reinforcement algorithm, one needs an environment and an agent and a feedback loop to connect both of them.
The simple learning behavior of the reinforcement machine learning method is used quite frequently in various platforms such as in video games, industrial simulations, resource management, etc.
Difference between machine learning and artificial intelligence
| Machine Learning | Artificial Intelligence |
|---|---|
| Machine learning can be defined as the ability of computers to acquire knowledge and learn new skills. | Artificial Intelligence can be defined as the ability of computers to acquire knowledge and perform tasks which usually require human interference. |
| Machine learning concerns about acquiring knowledge. | Artificial Intelligence concerns acquiring wisdom and intelligence. |
| Machine learning is simply a computer algorithm which acquires data and learns from data. | Artificial Intelligence is a computer program which uses its knowledge to perform smart tasks. |
| Machine learning tries to find a solution for a problem without worrying about whether it is the optimal solution or not. | Artificial intelligence tries to find the most optimum solution for the problem. |
| The primary purpose of machine learning is to increase the accuracy of the solution and less concerned about success. | The primary purpose of artificial intelligence is to get success at something and least concern about the accuracy of the solution. |
| Machine learning is all about creating self-learning algorithms. | Artificial Intelligence is about developing a system which can imitate human behaviour under different circumstances. |
| Machine learning algorithms learn maximum information on a particular task and use that knowledge to improve the performance of the machine to perform that task. | The goal of Artificial Intelligence is to improve the natural intelligence of the system to act more like humans to work in complex situations. |
| Machine learning algorithm is designed to learn new things. | Artificial intelligence is about decision-making |
| The three main categories of machine learning are unsupervised learning, supervised learning, and reinforcement learning. | Similarly, artificial intelligence can be divided into three categories, such as Weak AI, Strong AI, and General AI. |
| Machine learning algorithms learn and self-correct themselves when introduced to new data. | Artificial Intelligence performs reasoning, learning, and self-correction. |
| Machine learning usually uses structured and semi-structured data. | Artificial Intelligence uses structured, semi-structured, as well as unstructured data for learning purposes. |
| The examples of applications of machine learning are Google Search algorithm, Facebook’s auto friend suggestion or video suggestion, and the online recommendation system, etc. | The examples of the applications of Artificial Intelligence are Intelligent human robots, Siri, Alexa, and Google Assistant, expert systems, etc. |
Examples of Machine Learning
The following are examples of machine learning.
1. Virtual Assistant
The first example of machine learning is the one that you might be holding in your hand or is lying right next to you. Virtual assistants like Google Home, Amazon Echo, and smartphone virtual assistants like Siri in Apple and google assistant in android phones.
These work as personal assistants for individuals. All you need to do is teach them, and they will become your assistant. You can ask them to wake you up in the morning, to remind you to book tickets. Machine learning is an essential part of virtual assistants. It collects data and refines according to the involvement of the user.
2. Traffic prediction
All of us use google maps to travel in unknown territory. Google maps make the use of GPS navigation services to provide us with the right traffic-related information in a particular area. While we use it for our personal use, it collects records or information and generates aggregate information to provide information about the traffic situation in a particular area.
3. Social media services
Social media services make the use of machine learning to show posts on the users’ news feed, which is not only useful for the user but is also helpful for the service provider.
The following are the things that you must notice in your Facebook account.
1. People you may know
It suggests you new friends based on the people part of your friend list. Facebook learns about you through your interaction with the Facebook platform.
2. Face recognition
Facebook recognizes the face of people on the photos that you upload in your profile and based on that information, and it provides you with tagging suggestions. It might seem quite simple to you at the front end, but it is quite an intricate process at the backend, using which Facebook gives you the right suggestions.
3. Email spam filter
Have you noticed that now you don’t have to face hundreds of spam emails every morning when you open your email account? Now every email platform uses a spam filter feature that recognizes spam emails and adds them to a separate folder.
4. Product recommendations
Earlier, you might have thought that Facebook and Instagram read your mind and showed you ads of products that you were thinking. But it became possible just because of machine learning.
These social media apps make the use of machine learning algorithms and show relevant ads to the people in which they have shown interest in other platforms.
5. Search engine
Search engines like Google and Bing make the use of machine learning to provide better search results for the users. The algorithm analyzes your behavior on the app and shows you results based on your previous action.
For example, when you search something in the google search engine and open the top result of the search, then google learns that you have been provided with correct information. If you don’t open any result, then it means the information shown to you is not right, and it will try to changes its outcome for your future searches.
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