As the term might seem like, Machine Learning is a use of artificial intelligence that provides the computer systems the ability to automatically learn and improve the user experience without being explicitly programmed. Machine Learning mainly concentrates on the development of computer programs that can access data and use the same data to learn from them. In a rawer term, machine learning is the process of teaching a computer system how to make accurate predictions when data is fed into the system. It is an artificial intelligence discipline geared toward the technological development of human knowledge. Machine Learning allows computers to handle new situation via analysis, self-learning, self-training, observation and experience. Machine Learning allows for continuous progressive changes of computing. This is done by providing exposure of new scenarios. Testing these scenarios and then adapting them is an important part of Machine Learning. Machine Learning also works for implementing pattern and detecting trends for providing more accurate and improved decisions in specific situations. Machine Learning is often confused with another term called as Data Mining and knowledge discovery in databases, which share a similar methodology. It is quite difficult to replicate human intuition in a machine, primarily because human beings often learn and execute decisions unconsciously. Machines could be considered very similar to a human child. They require an extended training period when developing broad algorithms geared toward the dictation of future behavior. The main focus is to help the machines (computers) learn by themselves. There must be no human interference. These computers must be able to adjust the actions accordingly. Machine Learning vs Artificial Intelligence (AI) Machine learning has gained most of its success quite late, but it is just one method for achieving artificial intelligence. Artificial Intelligence was born in the year 1950s. AI is any machine’s capability to perform a task that would typically require a human intelligence. AI systems are known for some common features which they depict. These traits might be seen such as planning, learning, reasoning, and problem solving. The Artificial Intelligence system might also support features of knowledge representation, perception, motion, and manipulation. Most Artificial Intelligence systems tend to neglect Social intelligence and creativity. Machine Learning is a subset of Artificial Intelligence. Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computations. These are where algorithms undergo random mutations, and combinations between generation to “evolve” optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human in a specific domain. Types of Machine Learning algorithm Machine Learning can be divided into two categories: supervised and unsupervised learning.
Supervised machine learning algorithms: This algorithm basically teaches machines by some examples. During training for supervised learning, systems are exposed to large amounts of labelled data. When enough examples are provided to the system, this algorithm would allow the system to break the data received into clusters to recognize several patterns and give out some results. However, for training these systems, it requires huge amounts of labelled data. The amount of labelled data can extend to a million or a billion data.Unsupervised machine learning algorithm: Unsupervised learning is just the opposite to the supervised leaning algorithm. In this algorithm, we do not have the data sets labelled or classified. The systems learn by identifying pattern that constantly appears in the data that is fed to it. The system tries to identify a similarity in the pattern and then determines / predicts a result. It cannot be used on a specific kind of data. It focuses on data that tend to have similar pattern.
There are some other kinds of learning algorithm as well that may be classified as follows:
Semi-supervised learning: The main disadvantage of using a huge set labelled dataset is that it might disperse over time. This algorithm tends/tries to make a combination of both the labelled and unlabelled data, i.e. tries to use supervised as well as unsupervised learning. The learning process combines the use of both labelled and unlabelled data sets to train the systems. The labelled data trains the model partially and then this partially trained model is then used to train the unlabelled set of data.Reinforcement learning: Reinforcement learning is basically teaching the system by a complete trial and error method in which the system is exposed a given environment. It starts to interact with the environment and starts to produce actions and discovers errors or rewards. Trial and error search and delayed rewards are the most relevant characters of this algorithm.
Machine Learning is not that easy a concept and requires a lot of patience to learn because the above-mentioned categories of algorithm are further subdivided into many other smaller algorithms. We hope to have got your basic concept about Machine Learning clear. Enjoy Learning!!
