Machine Learning

Machine Learning  

Machine Learning is a new trending field these days and is an application of 
artificial intelligence. 
It uses certain statistical algorithms to make computers work in a certain way without being explicitly programmed. The algorithms receive an input value and predict an output for this by the use of certain statistical methods. 
The main aim of machine learning is to create intelligent machines which can think and work like human beings.

Requirements of creating good machine learning systems

Data - Input data is required for predicting the output.

Algorithms - Machine Learning is dependent on certain statistical algorithms to determine data patterns.

Automation - It is the ability to make systems operate automatically.

Iteration - The complete process is an iterative i.e. repetition of the process.

Scalability - The capacity of the machine can be increased or decreased in size and scale.

Modeling - The models are created according to the demand by the process of modeling.

Methods of Machine Learning

The methods are classified into certain categories. These are:

Supervised Learning - In this method, input and output is provided to the computer along with feedback during the training. The accuracy of predictions by the computer during training is also analyzed. The main goal of this training is to make computers learn how to map input to the output.

Unsupervised Learning - In this case, no such training is provided leaving computers to find the output on its own. Unsupervised learning is mostly applied on transactional data. It is used in more complex tasks. It uses another approach of iteration known as deep learning to arrive at some conclusions.

Reinforcement Learning - This type of learning uses three components namely - agent, environment, action. An agent is the one that perceives its surroundings, an environment is the one with which an agent interacts and acts in that environment. The main goal in reinforcement learning is to find the best possible policy.

Applications of Machine Learning

Following are some of the applications:

1. Cognitive Services
2. Medical Services
3. Language Processing
4. Business Management
5. Image Recognition
6. Face Detection
7. Video Games 

Benefits of Machine Learning

Everything is dependent on these systems. Find out what are the benefits of this.

Decision making is faster - It provides the best possible outcomes by prioritizing the routine decision-making processes.

Adaptability - It provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.

Innovation - It uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.

Insight - It helps in understanding unique data patterns and based on which specific actions can be taken.

Business growth - With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.

Outcome will be good - With this the quality of the outcome will be improved with lesser chances of error.