How

1. Introduction:

              The important aspect of AI is the control policy of the agent which implies how the inputs obtained from the sensors are translated to the actuators, in other words how the sensors are mapped to the actuators, this is made possible by a function within the agent.

              The ultimate goal of AI is to develop human like intelligence in machines. However, such a dream can be accomplished through learning algorithms which try to mimic how the human brain learns.

              In the area of machine learning research, the emphasis is given more on choosing or developing an algorithm and conducting experiments on the basis of the algorithm. Such highly biased view reduces the impact or real world applications.

2. MACHINE LEARNING :

                According to Arthur Samuel, Machine learning is defined as the field of study that gives computers the ability to learn without being explicitly programmed. Arthur Samuel was famous for his checkers playing program. Initially, when he developed the checkers playing program, Arthur was better than the program. But over time, the checkers playing program learned what were the good board positions and what were bad board positions are by playing many games against itself.

3. TYPES OF MACHINE LEARNING ALGORITHMS:

•                    Supervised Learning
•                    Unsupervised Learning
•                    Reinforcement Learning
•                    Recommender Systems    

4. APPLICATIONS OF MACHINE LEARNING AND LITERATURE SURVEY:

• Unsupervised Learning
• Supervised Learning
• Recommender Systems 
• Reinforcement Learning

5. IMPRESSION AND VIEWS:

        In Machine, learning the artificial agents learns from training data or by interacting with the environment and influences it to facilitate the best possible result. So, Machine Learning, is definitely a subfield of Artificial Intelligence. This notion has made the present day applications autonomous.

         As in machine learning, supervised and unsupervised learning are of the two major types. And AI agents are general problem solvers and can be applied in various fields.

6. Conclusion:

         Humans have always sought to build a comfortable life, the proof of this lies in the fact that we have always depended on machines to get our work done more easily, in a faster and more efficient manner. In the past machines have been used to reduce the manual labor required to get a job done, but at present, with the advent of machine learning humans seek to build machines which are not only strong but also intelligent and hence machine learning has emerged to become an area of study that is ever in the bloom. Machine learning has not just made the machines autonomous, bringing forward the concept of autonomous computing, but it has also reduced the constant vigilance users are required to keep upon the applications. In this paper, discusses the four categories of machine learning i.e. supervised learning, unsupervised learning, and reinforcement learning and recommender system and also presents the numerous applications under them. Apart from that, two proposed applications namely information time machine and virtual doctor have been put forward.

7. ACKNOWLEDGMENTS:

              Our thanks to the experts Dr. Susanna Biswas, Kalyan University, who have advised and encouraged us for such kind of development. Also, our special thanks to Dr. Som subhra Gupta, J IS College of Engineering for providing all kinds of required resources.