Companies all throughout the world are automating their procedures for gathering, analyzing, and visualizing data. To minimize human labor and stay ahead of the curve, they are also consciously implementing artificial intelligence into their business plans. A branch of artificial intelligence known as machine learning has emerged as one of the most sought-after employment choices. Experts employ this technique for data analysis to automate the creation of analytical models. Thanks to machine learning, systems are constantly changing and learning from data, finding patterns, and offering insightful information with little assistance from humans.
Types of Machine Learning
The four different types of machine learning are:
#1: Supervised Learning
In this type of machine learning, machines are trained using labeled datasets. Machines use this information to predict output in the future. This whole process is based on supervision and hence, the name. As some inputs are mapped to the output, the labeled data helps set a strategic path for machines. Moreover, test datasets are continuously provided after the training to check if the analysis is accurate. The core objective of super learning techniques is to map the input variables with the output variables. It is extensively used in fraud detection, risk assessment, and spam filtering.
Let’s understand supervised learning with an example. Suppose we have an input dataset of cupcakes. So, first, we will provide the training to the machine to understand the images, such as the shape and portion size of the food item, the shape of the dish when served, ingredients, color, accompaniments, etc. After completion of training, we input the picture of a cupcake and ask the machine to identify the object and predict the output. Now, the machine is well trained, so it will check all the features of the object, such as height, shape, color, toppings, and appearance, and find that it’s a cupcake. So, it will put it in the desserts category. This is the process of how the machine identifies various objects in supervised learning.
Supervised machine learning can be classified into two types of problems:
Classification
When the output variable is a binary and/or categorical response, classification algorithms are used to solve the problems. Answers could be – Available or Unavailable, Yes or No, Pink or Blue, etc. These categories are already present in the dataset and the data is classified based on the labeled sets provided during training. This is used worldwide in spam detection.
Regression
Unlike classification, a regression algorithm is used to solve problems where there is a linear relationship between the input and output variables. Regression is used to make predictions like weather, and market conditions.
Here are the Five Common Applications of Supervised Learning:
- Image classification and segmentation
- Disease identification and medical diagnosis
- Fraud detection
- Spam detection
- Speech recognition
#2: Unsupervised Learning
Unlike the supervised learning technique, here there is no supervision involved. Unlabeled and unclassified datasets are used to train the machines. They then predict the output without supervision or human intervention. This method is commonly used to bucket or categorize unsorted data based on their features, similarities, and differences. Machines are also capable of finding hidden patterns and trends from the input.
Let us look at an example to understand better. A machine might be provided with a mixed bag of sports equipment as input. Though the image is new and completely unknown, using its learning model the machine tries to find patterns. This could be color, shape, appearance, size, etc to predict the output. Then it categorizes the objects in the image. All this happens without any supervision.
Unsupervised learning can be classified into two types:
Clustering
In this technique, machines bucket the data based on the features, similarities, and differences. Moreover, machines find inherent groups within complex data and ensure object classification. This is commonly used to understand customer segments and purchasing behavior, especially across geographies.
Association
In this learning technique machines find interesting relations and connections among variables within large datasets that are provided as input. How is one data item dependent on another? What is the procedure to map variables? How can these connections result in profit? These are the main considerations in this learning technique. This algorithm is especially popular in web usage mining and plagiarism checking in doctoral work.
Four Common Applications of Unsupervised Learning
- Network analysis
- Plagiarism and copyright check
- Recommendations on e-commerce websites
- Detect fraud in bank transactions
#3: Semi-Supervised Learning
This technique was created keeping the pros and cons of the supervised and unsupervised learning methods in mind. During the training period, a combination of labeled and unlabeled datasets is used to prepare the machines. However, in the real world, most input datasets are unlabeled data. This method’s advantage is that it uses all available data, not only labeled information so it is highly cost-effective. Firstly, similar data is bucketed. This is done with the help of an unsupervised learning algorithm. This helps label all the unlabeled data.
Let us take the example of a dancer. When the dancer practices without any trainer’s support it is unsupervised learning. In the classroom, however, every step is checked and the trainer monitors progress. This is supervised learning. Under semi-supervised learning, the dancer has to follow a good mix. They need to practice on their own but also need to revisit old steps in front of the trainer in class.
Semi-supervised learning falls under hybrid learning. Two other important learning methods are:
Self-Supervised learning
An unsupervised learning problem is framed as a supervised problem in order to apply supervised learning algorithms to solve it.
Multi-Instance learning
It is a supervised learning problem but individual examples are unlabeled. Instead, clusters or groups of data are labeled.
#4: Reinforcement Learning
In reinforcement learning, there is no concept of labeled data. Machines learn only from experiences. Using a trial-and-error method, learning works on a feedback-based process. The AI explores the data, notes features, learns from prior experience, and improves its overall performance. The AI agent gets rewarded when the output is accurate. And punished when the results are not favorable.
Let us understand this better with an example. If a corporate employee has been given a completely new project then their success will be measured based on the positive results at the end of the stint. In fact, they receive feedback from superiors in the form of rewards or punishments. The office is the environment, and the employee carefully takes the next steps to successfully complete the project. Reinforcement learning is widely popular in game theory and multi-agent systems. This method is also formalized using Markov Decision Process (MDP). Using MDP, the AI interacts with the environment when the process is ongoing. After each action, there is a response and it generates a new state.
Reinforcement Learning can be Categorized into Two Methods:
- Positive Reinforcement Learning
- Negative Reinforcement Learning
How is Reinforcement Training Used in the Real World?
- Building intelligent robots
- Video games and interactive content
- Learn and schedule resources
- Text Mining
Real-World Application of Machine Learning
Machine learning is booming! By 2027, the global market value is expected to be $117.19 billion. With its immense potential to transform businesses across the globe, machine learning is being adopted at a swift pace. Moreover, thousands of new jobs are cropping up and the skills are in high demand.
Here are a Few Real-World Applications of Machine Learning:
- Medical diagnosis
- Stock market trends and predictions
- Online fraud detection
- Language translation
- Image and speech recognition
- Virtual smart assistants like Siri and Alexa
- Email filtering especially spam or malware detection
- Traffic prediction on Google maps
- Product recommendations on e-commerce sites like Amazon
- Self-driving cars like Tesla
Every user today generates nearly 2 Mbps of data. In this data-driven world, it is increasingly important for businesses to digitally transform and keep up. By analyzing and visualizing data better, companies can have a good competitive advantage. In order to stay ahead, companies are constantly looking for top talent to bring their vision to life.
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