Machine learning is a branch of computer science, and Artificial Intelligence (AI) focused on the use of algorithms and data to copy what humans learn. Over time, the accuracy of machine learning increases.
There are plenty of machine learning services applications. This include:
- Product Recommendation: Machine learning is used to recommend products that fit your purchasing behavior.
- Virtual Assistant: Virtual assistants such as Siri and Alexa use machine learning to comprehend questions and commands.
- Security Operations Centre: Machine learning can analyze files in a short period of time, making it easier to detect anomalies. The feature can help identify security incidents quicker.
You might not realize it, but you are actually benefiting from plenty of machine learning services. Service providers such as Mphasis help make daily life easier and convenient with robust technology solutions.
However, did you know that there are different types of machine learning? Read this article to discover the primary classifications of machine learning.
Different Types of Machine Learning
There are 3 main classifications of machine learning methods. These are:
Supervised Machine Learning
Supervised machine learning is the use of labeled datasets to train algorithms predicting outcomes and classifying data accurately. In this classification, input data is provided into the model, which adjusts its weight until the fit is appropriate. This happens as part of the cross-validation process.
Some examples of supervised machine learning models include:
- Predictive Analytics
Plenty of supervised machine learning models are used in building predictive analytic systems. Its main purpose is to provide in-depth insight into data points of businesses. The insights from predictive analytics allow decision-makers to make informed decisions accurately.
- Image and Object Recognition
Supervised learning algorithms are used in image and object recognition systems. These algorithms can identify, categorize and isolate objects from images and photos. These features make them useful for imagery analysis and computer vision techniques.
- Spam Detection
Supervised learning algorithms are trained to recognize anomaly patterns in new data to categorize spam and non-spam correspondences accurately.
Supervised machine learning has many applications that can help make everyday life easier and more convenient. However, there are also several disadvantages to using it, such as:
- Steep learning curve when it comes to structuring supervised learning models accurately
- Time intensive training
- Datasets can be affected by human error, causing the algorithm to learn inaccurately
- Supervised learning models are not able to classify data on it’s own, so it can be more labor-intensive
Unsupervised Machine Learning
There’s a lot of confusion about supervised and unsupervised machine learning. However, there’s a clear distinction between these two, and that’s the fact that unsupervised machine learning uses unlabeled data, unlike supervised machine learning.
Using unlabeled data, unsupervised machine learning algorithms uncover patterns that offer solutions to association or clustering problems. This is significantly useful when the common properties within a data set are unsure.
Unsupervised machine learning can be used for the following:
- Medical Imaging
Unsupervised machine learning offers important features to medical imaging equipment, including image detection, segmentation, and classification. These are mostly applied in pathology and radiology, making patient diagnosis more accurate and fast.
- Anomaly Detection
Unsupervised machine learning algorithms can analyze bulk data and detect atypical data points. Being able to detect anomalies quickly makes it easier for companies to address human error, faulty equipment, or security breaches.
- Computer Vision
Object recognition can also benefit from unsupervised machine learning algorithms by making visual perception tasks faster and more accurate.
- News Section
Google News utilizes an unsupervised machine learning algorithm to categorize articles about the same story from different online news outlets. This makes it easier for users to find results relevant to the topics that they are searching for.
- Customer Personas
A clear buyer persona is essential for businesses, especially when creating making and sales strategies. Unsupervised machine learning algorithms can help businesses identify and understand the common traits of their customers. This way, businesses can create products and strategies that resonate with their target buyers.
However, despite its wide range of applications, unsupervised machine learning also has its fair share of disadvantages. These include:
- Computation complexity caused by a high volume of data training
- It takes a longer time to train
- There’s a high risk of inaccurate results, especially at the beginning
- Lack of transparency in terms of what are the basis used when clustering data
Reinforcement Learning
This type of machine learning is said to have the closest similarity to how humans learn. Here, algorithms used in learning interact with their environment, allowing it to receive positive or negative rewards.
The common applications of reinforcement learning include:
- Marketing
Most marketing tasks involve promotion to sell products and services. In this process, finding the right audience is essential, which is what reinforcement learning can help you with. This model can predict reactions, choices, and even customer behavior.
- Healthcare
Doctors can use reinforcement learning models to identify a patient’s most suitable treatment type. It also helps in administering the proper drug dose and determining such drug doses.
- Image Processing
Reinforcement learning is also useful in image processing. Image processing is the practice of improving the current version of an image to gain useful information.
- Broadcast Journalism
Broadcast journalism uses various reinforcement learning models to track reader behavior. This enables their sites to provide you with recommendations tailored to your behaviors and patterns.
- Robotics
Reinforcement learning is also widely used in the field of robotics. It helps robots learn how to grasp visible objects and solve complicated tasks that most humans cannot do.
- Gaming
Gaming has become a big part of many people’s lives. Reinforcement learning optimizes game algorithms, providing gamers with a better overall experience. This is possible because reinforcement learning helps create prediction models that learn how to win in complex situations.
Final Words
Learning about the different types of machine learning will make you realize that its application is all around you. At the same time, you’ll also understand that it has so much potential to make life convenient and easier in the future. With machine learning, perhaps the idea of robots and automation being the norm is not so far fetched.
Mphasis 