Machine learning is a significant area of technology, as you are aware. The majority of technology today is evolving toward artificial intelligence (which are created on Machine Learning). The majority of businesses are more focused on AI. Deep Learning is a different, more sophisticated sort of learning. You need to grasp what machine learning and AI are before diving further into these four forms of machine learning.
AI is the result of a human or machine learning algorithm building a collection of neutral networks. It resembles the brain. It is comparable to a being who does not require air to breathe, who never gets weary, and who does not require food. AI is a part of our daily lives. Even when you search something on Google, AI is responsible for returning results that are relevant to your query. AI is the reason Tesla cars have Autopilot. One type of AI is the JARVIS voice system from the Iron Man movie.
Some definitions for AI:
- “… is the science and engineering of making intelligent machines” — (original definition by John McCarthy who coined the term ‘Artificial Intelligence’ in 1955)
- “… is an intelligent machine” — (Google’s Avinash Kaushik)
- “… anything that makes machines act more intelligently” (IBM‘s definition)
If you want to read more definitions for AI, read Here.
Some helpful resources to read about AI:
A technology called “machine learning” enables a computer or other machine to carry out a task on its own. Due to machine learning, a machine can learn on its own utilizing his experiences, past data, and predicted data. The amount of data that machine learning can access is crucial. Therefore, our machine learning system becomes better the more data it receives.
Some definitions for Machine Learning:
- “This introduction to machine learning provides an overview of its history, important definitions, applications, and concerns within businesses today.” — IBM
- “Machine learning is a subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data.” — Google
Some helpful resources to read about Machine Learning:
Artificial intelligence (AI) instructs a computer or other machine to perform tasks without the assistance of a person, and machine learning identifies patterns and rules to improve AI. Machine Learning is the subset of AI.
Machine learning comes in a variety of forms. There are reportedly 4 types, 3 types, and even 14 types of machine learning, according to some. But four different methods of machine learning are generally accepted. There are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Transfer Learning
Some claim that semi-supervised learning should be included.
You need data and labels in order to build a machine learning model (algorithm). The model is taught to understand how data and labels relate to one another. The model is trained on input and output, which is another way to define it. For instance, based on a person’s data, we can determine whether they have heart disease or not. By entering a photo, we can categorize it as either a dog or a cat.
There are two main types of Supervised Learning:
Classification — involves predicting a class label, for examples— heart disease problem, movie genre prediction
Regression — involves predicting a numerical label, for examples — some price prediction projects, marketing outcome prediction projects
If you want to read more about supervised learning, click here.
If you have a large amount of data but no labels for it. In this situation, the machine learning model tries to extract or detect the relationship in the data without using labels. Unsupervised learning does not have output data or target data, in contrast to supervised learning.
Unsupervised learning also comes in a variety of forms, but the primary two are as follows:
Clustering — finding group in data, for examples — Spotify Music Recommendation System, Customer Segmentation
Density Estimation — involves summarizing the distribution of data
If you want to read more about unsupervised learning, click here.
Reinforcement learning is a process where an agent interacts with his surroundings and figures out how to act on his own based on what he has learned. There is no pre-collected data or outputs because of the utilization of the environment. The algorithm gains knowledge through practice.
For example, an agent could be a chess player, it can play chess with a real person. It improves its knowledge while playing chess.
If you want to read more about reinforcement learning, click here.
Transfer learning is simple. If you already have a model that has been trained on a particular set of data and can be used to a problem of a similar nature. As an illustration, suppose you trained a model to extract the relationships between the words in Wikipedia and then used that model on your own text or documents file.
If you want to read more about Transfer Learning, click here.
It is a wise decision to read about different forms of machine learning whether you are a machine learning engineer or are interested in AI. There are many materials available online. You can read about it in any random blog article.
My explanation can be wrong. I tried my best to write this blog. If you want to point out my mistakes or suggest something to me, please feel free to email me. I am warmly welcome. I will always be thankful to you.
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