Types of Machine Learning Algorithms | Machine Learning Consulting

With the technology advancement, we have often come across the terms like machine learning Deep learning or Artificial intelligence. Machine learning is under the umbrella of the AI which is the process of teaching the machine to operate on its own.

But how the machine is being taught about the different operation?

So different algorithms and mathematical expressions are used for the data analysis and the machine learning technique. But before starting the explanation of the different algorithm forms, some terms need to be defined.

• labeled data: a training data which consists of a pair of data- input and output sample
• Classification of the value which should be discrete.
• Regression whose goal is to predict the value continuously.

Different techniques to know how to teach a machine

Now we will be coming to the explanation of the different types of the algorithm use of rather machine learning:

Supervised learning

• It is an algorithm which is trained and a process with sample input and output is picked. But to feed the algorithms human experts are compulsory in this type. The types of algorithms in this learning technique are- nearest neighbor, naive Bayes, decision trees, linear regression, supportive vector regression, and neural networks.

Unsupervised learning

• Here the machine is trained with the unlabeled data where no role is played by any human experts. Algorithms of pattern description and the descriptive modeling are normally used. These algorithms have no output categories. Clustering algorithm and the association rule learning algorithms are the main types. Also the K- means clustering, an association rule is a common algorithm.

Semi-supervised learning

• This in between the two above mentioned. Using the labeled data may need the human experts whose costs are high. So here some cases are labeled while some are unlabelled. This algorithm is considered best for the model building.

Reinforcement learning

• It is targeted to gather information as the observations from the different interaction with the environment. Based on this observation, the necessary action is being taken by the machine. Particularly this algorithm learns from the environment that is known as the agent. The learning goes in an iterative process until the full possibilities are gained.

This is also considered as the branch of the artificial intelligence. On getting a specific type of problem, the reinforcement learning starts the loop. The output I being chosen based on the current status of the algorithms. The steps which continue these actions:

• Agents observe the input state
• An agent takes an action only by the decision-making function
• Agents get a reward or better termed as reinforcement from the environment
• The state- action pair is saved for later use.

NOTE: Algorithms used are the - Q learning, Temporal difference, Deep adversarial network.

Conclusion

Different criteria are there to classify the different algorithms. But to know the best suitable one, the learning technique should be seen through the big picture. Whatever process you use for the learning, data is the main thing. 

Based on this the technique differs on the availability of the data. Sometimes You can face many problems in machine learning then you can visit the best machine learning consulting companies in India. Feel free to contact us.

Read More About:

• Machine Learning Business Applications.