Machine learning is the systematic study of algorithm and statistic analysis which is used by the computer system to complete a particular task and give more accurate outcomes, without using any external program or instructions. Machine learning is a division of artificial intelligence.
Machine learning algorithms are mainly categorized into the following parts
Supervised ML algorithm
The supervised algorithm generally needs a data scientist or data analyst who has machine learning skills. During algorithm training, data analyst need to present input, output as well as feedback about the correctness of future predictions. The model analyzes only those features and variables which are decided by the data analyst. This algorithm has also the ability to compare its output with correct output and if any error found, that also modify
accordingly.
The most commonly used supervised ML algorithms are:
- Decision Trees
- Native Bays
- Linear regression
- Random forest
Unsupervised ML algorithm
This algorithm is also known as a neural network. This algorithm does not need any desired outputs, they generally use deep learning method to check the data and turn up at the conclusion. This algorithm is generally used for complex tasks like image reformation, speech-to-task, etc. this algorithm knows how to understand a function to explain an unseen function from unlabeled data.
The most commonly used unsupervised ML algorithms are:
- K-means clustering;
- Principle component analysis.
Semi-supervised ML algorithm
This algorithm falls between the above two. In many situations, the cost of the process is very high. At that time it requires skilled human help. So in the lack of labels, semi-supervised ML algorithm is best.
Reinforcement ML algorithm
This algorithm is all regarding develop a self-sustained system that, during infectious series of tried and fails.
- Q-learning;
- Monte-carlo search
Nowadays machine learning companies in India are very popular because India is famous for efficient ML developers. The competition level of ML is rising day-by-day. So we need to up-to-date about this field.
Following are some advantages and disadvantages of ML
Advantages of Machine Learning Algorithm
Simply recognize trends and patterns: Used in a variety of applications like financial sectors, banking, healthcare, social media, etc. In all the above applications ML algorithm helps to understand the browsing behaviour as well as a storied history of its users to find the right information.
No human interference required: This algorithm gives a chance to the machine to predict future outputs and change the algorithm as per requirement. For example anti-virus in our system. They found a new thread in the system at its own.
It supply continues quality improvement in a large and complex environment.
Manage a large amount of multi-dimensional and multi-variety data in dynamic and unsure environments.
Disadvantages of Machine Learning Algorithm
- Acquisition of appropriate data is a big challenge for the ML algorithm. Because it needs lots of good quality data for processing.
- Need a large number of resources and enough time to learn and develops an algorithm to execute the required purpose and maintain accuracy.
- Ability to accurately understand the results is also a big challenge for the ML algorithm.
- High-error inclination: it’s an autonomous algorithm so there may be a huge possibility to occur an error in it.
Future of Machine learning algorithm
In just a few years machine learning will be a part of almost every software application. Machine learning service provider will become very common in the future and this technology will become quite mature. Almost
all businesses will start using the service. ML helps computers to understand the human language more efficiently, making them work more like as a human.
Machine Learning algorithm can work amazingly powerful when a large amount of training data is available. Combination of machine learning and artificial intelligence are capable of delivering more effective information.
