Article on Machine Learning
In today's time, the field of science is making a lot of progress. Everything from pen to laptop is a gift of science. Our world is full of gadgets and machines. In the development of human society, science has played a major role. The Computer is one of these amazing discoveries, which have affected human life in almost all fields. In the early days, computers were not so capable but due to continuous development, today computer has become our need in every work. In the coming time, the machine age is about to start or we can say, it has already started, where computers have the ability to think like humans.
What is Machine learning?
Machine learning is a part of artificial intelligence that enables the system to automatically learn and improve itself when needed.
Machine learning has the power to learn to program automatically without having to program. In this, the system is made to work so efficiently that the machine can complete tasks on its own from next time, on the basis of its previous experience and continuously improves itself over time. Just like we humans do. We learn something or the other from all our good and bad experiences and do some work in future based on that experience. The concept of machine learning is built on this basis. That is, the computer or machine is programmed in such a way that it works according to the user's mind, as well as to store the user's commands and data associated with it.
Machine learning develops on computer learning programs by accessing data for itself and later using it to learn from it. The process of machine learning begins with observing the data, looking for patterns in the data obtained by the machine through direct experience or instructions by users and take better decisions in future. The main purpose behind machine learning is that the computer should learn on its own and do the work according to it without the help of any human. In simple word, human wants to make machine, which thinks like human himself.
How does machine learning works?
Machine learning is the part of Artificial Intelligence, which teaches computers to think about ways of thinking similar to humans, such as learning and improving from past experiences. It works by exploring, investigating data and identifying patterns and involves minimal human intervention. A key aspect of what makes machine learning valuable is it's ability to discover what and which information has been left out of human eyes while collecting data. Machine learning helps in determining the complex patterns that can potentially be ignored in human analysis. To understand how machine learning works, it is very important to understand it's algorithms.
- Supervised machine learning
- Unsupervised machine learning
- Semi-supervised machine learning
- Reinforcement machine learning
Supervised machine learning
In this algorithm, the machine reads from the past experiences, and applies it to the new data. So that it can predict the events that will happen in the future by using the previous given examples. This algorithm works in exactly the same way that humans actually learn with their past experiences. In supervised learning, various types of examples and answers are given as input to the machine; this algorithm learns from these examples and guesses the correct output based on the given inputs.
Supervised machine learning is further classified into:
The machine learning must draw conclusion from existing data and should predict to which class new data belongs. For example, new emails are filtered as spam and non-spam based on previous data.
Most popular classification algorithms are:
- Naive Bayes
- Logistic Regression
- KNN Model
- Support Vector Machines (SVM)
In this algorithm, the machine is taught to identify and predict relationship among one dependent and other series of independent variables.
Some regression algorithms are:
- Linear Regression
- Ridge Regression
- Neural Network Regression
- Lasso Regression
- Decision Tree Regression
- Random Forest
- KNN Model
- Support Vector Machines (SVM)
- Dimensionality Reduction Algorithms
Gradient Boosting algorithms
Predicts future by analyzing the past and present data. Type of Forecasting methods are:
- Qualitative Methods
- Quantitative Methods
- Average Method
- Naive Method
- Drift Method
Unsupervised machine Learning
Unlike supervised machine learning algorithms, examples and answers are not given in advance. In this, the algorithm machine itself has to predict based on the data. Therefore, algorithms learn from test data or real data, which have not already been labeled, categorized or classified.
In unsupervised machine learning, it identifies similar data sets and each new piece of data gives an output based on the presence or absence of such similarities.
Grouping of similar dataset is done based on defined criteria, by identifying pattern in each dataset.
- Agglomerative clustering
- Divisive clustering
- K-Means Clustering
- DBSCAN Clustering
- Hierarchical Clustering
In dataset, numbers of input variables (dimensionality) are reduced. Instead of using one best algorithm for all cases, it explores range of dimensionality reduction algorithm.
Semi-supervised machine Learning
Semi-supervised machine learning, this algorithm lies in between both supervised machine learning and unsupervised machine learning. Because for learning, this algorithm uses both labeled and unlabeled data. The system which uses this algorithmic method is able to improve its learning ability very easily over time.
Reinforcement machine learning
Reinforcement machine learning is a learning method that interacts with the environment around it by presenting actions and detects errors as well. The speciality of this algorithm is to find and detect trial and error. This method helps the machine and S/W agents to automatically detect the activities of a particular instruction, thereby further improving the performance of the system.
Where machine learning is used?
By using machine learning, translator technologies such as Google Translator or Microsoft Transalator are doing things like user taking a photo of a sign board on the road or the menu written in any language, it finds the words and the language present in it and translates it in user language. With this, user can speak in any language and speech recognition technology leveraging machine learning can start its work of recognizing the spoken words. Speech Recognition is used in other products like in Google apps and ANSHIA app you can ask any question in your voice, also in YouTube, you can speak and search the videos you want.
Machine learning is being used many apps such as shopping website, email spam filter, Facebook etc. Facebook is widely used around the world and based on face detection and image recognition; it checks in its database and recognizes a photo or image and machine learning is used in this automatic friend tagging suggestions in Facebook. While using email, you must have noticed that the emails we need arrives in the inbox and the most of the mails goes to the spam folder. In which machine learning automatically detects any content and source and if something is found wrong, spams the email.
Customer Analysis In retail, where trade can be easily understood, and future sales can be predicted. Also, by understanding the browsing history behaviour of the customer, suitable products can be suggested on their screen, thereby increasing the customer experience and increasing sales.
Fraud detection Machine learning is also being used in the finance sector. Such as online money transactions, increasing security and preventing fraud activities.
Healthcare Industry Machine learning is also evolving very fast in the healthcare industry. Machine learning helps to detect the disease of humans through physical activities, also health facilities are promoted at a very low cost.
Industrial Automation or Factory Automation is applying Machine learning on the large volume of data being generated by IoT devices in Industry 4.0 implementation.
Recommendation engines Google and Facebook use machine learning to show better ads to the user. When you do online shopping, you must have noticed that the information related to your searched product starts appearing everywhere. If you have searched for some product in Amazon and after some time when you open Facebook or YouTube, you start seeing advertisements related to that product. All this you see is actually due to machine learning, in which companies like Google, Facebook and Amazon keeps an eye on your every activity. All these ads are based on the previous search behaviour and this technique is called targeted advertising.
Virtual assistants Famark ANSHIA, Apple SIRI, Amazon ALEXA and Google Assistant are interactive voice-based virtual assistants and have become household name. Virtual assistant uses individual language to answer voice queries and commands like scheduling meeting, setting alarm, playing audio and videos requested many more.
Benefits of machine learning
- Identifies trends and patterns easily.
- No human intervention needed (automation).
- Since machine learning has the ability to learn on its own, improves the algorithm continuously over time.
- Handles multi-dimensional and multi-variety data.
- Wide Applications.
Drawbacks of machine learning
- Huge volume of datasets are required to train on.
- Considerable amount of time is required to achieve accuracy.
- Choosing correct algorithm to get required results.
- If small amount of not inclusive data set is used to train algorithm then it is highly susceptible to errors.
In conclusion the interaction of machine learning has made human life very easy, where machine learning is being used continuously to improve work in every field. This continuous use of machine learning is not limited to any one area but it benefits in almost every field. Machine learning is likely to be used in the future, in which the role of artificial intelligence is being considered very important.
Join Famark Community!
Famark community is a social platform for creative and innovative professionals from different domains.Join Community