Machine Learning refers to the development of software that improves automatically by experience in a given domain. It is generally regarded as a member of artificial intelligent systems. Recently, the machine learning has turned into one of the buzzwords of the IT industry. Machine Learning involves various areas like Designing, Data Mining, Decision Making, etc.
Machine Learning is also known as supervised learning, unsupervised learning and reinforcement learning. In the case of Machine Learning, the process is divided into two major categories: Algorithms and Software. The main benefit of Machine Learning is that the results are generated without the involvement of human, which helps in reducing the risk of system failures. As we all know, human interaction can lead to substantial deviation in the results obtained from the training algorithm. In addition to this, the artificial intelligence of the computer is also helpful in the training of algorithms.
The Machine Learning techniques are widely used in many domains including gaming, stock trading, medicine, recognition, product design, e-commerce, transportation, finance, etc. Today, there are a wide range of technologies that have been developed for Machine Learning. Some of the popular examples include the Recurrent Neural Networks (RNN), Convolutional Networks (CNN) and Deep Reinforcement Learning (DRL). RNN can be used to create models for image recognition, speech recognition and natural language understanding. CNN and DRL are widely used for data mining, product classification, supervised learning and web search.
The artificial intelligence machine learning methods can also be used to create highly efficient decision making tools. For example, you can train your AI assistant to make decisions on the basis of keyword and image search. Similarly, you can ask your assistant to predict the price movements in the Dow Jones Industrial Average by using financial news data and current real-time data available on the internet.
The key advantage of using these Machine Learning algorithms is that they make use of historical data to form a predictive database which is then used for decision making purposes. They enable programmers to create intelligent systems that are capable of taking on high level responsibilities. As the developers need not always be a computer expert, the programming environment becomes more open. Many top names in software development such as Facebook, Google, Amazon, Twitter and Baidu have created their own machine learning applications that are now in demand.
You can get highly trained systems for a little higher amount as they are quite accurate and can be applied for complex tasks requiring a high level of knowledge and understanding. The best Machine Learning Algorithms is trained on large databases and then modified on a regular basis to take into account new inputs. They also form a parallel artificial brain that learns on its own from past mistakes and inputs. In the case of large databases, the final result can be remarkably accurate. However, the final cost of running the Machine Learning Algorithms can be extremely high due to the large size and the overfitting of the training data.
The concept of Machine Learning Algorithms has received a lot of popularity among those involved in industries like finance and insurance, retail, sales and service and so on. They use such Machine Learning Algorithms for creating decision making applications which can be directly used by the end-user or can be indirectly used in a corporate setting. This is one of the reasons why artificial intelligence research is rapidly increasing. These Machine Learning algorithms can directly or indirectly help with various business processes like decision making, sales management, customer service and product forecasting etc. With the help of Machine Learning, it is possible to create AI assistants which can perform the repetitive tasks and also make inferences based on the large amounts of data.
For example, if you want to create an algorithm that predicts how many pairs of shoes will be sold during a particular week, you could either use the Kelly Criteria or The Moore Criteria. However, in this particular example we have artificially trained system which gives correct predictions without any human intervention. Thus, the importance of Machine Learning Algorithms is increasing because these are capable of predicting various parameters without any human guidance and this in turn makes it easier for business enterprises to predict their future sales and hence the amount of revenue that can be generated. So, this is what we can understand about the importance of Machine Learning Algorithms and what are the solutions available in the form of software’s and machine learning libraries which can be purchased for increasing the accuracy level of these Machine Learning algorithms.