Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that we make use of daily.

Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that work so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages.

Every time Facebook is used and it recognizes friends’ photos, that’s also machine learning. Spam filters in email save the user from having to wade through tons of spam emails, that’s also a learning algorithm. In this paper, a brief review and prospect of the vast applications of machine learning have been made.

1. INTRODUCTION: Intelligent Agent is an Artificial Intelligence (AI) program. Intelligent agent gets to interact with the environment. The agent can identify the state of an environment through its sensors and then it can affect the state through its actuators. 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.
2. The ultimate goal of AI is to develop human-like intelligence in machines. However, such a dream can be accomplished through learning algorithms that try to mimic how the human brain learns. Machine learning, which is a field that had grown out of the field of artificial intelligence, is of utmost importance as it enables machines to gain human-like intelligence without explicit programming. However, AI programs do more interesting things, such as web search, photo tagging, or email anti-spam. So, machine learning was developed as a new capability for computers, and today it touches many segments of the industry and basic science.
3. There are autonomous robotics and computational biology. Around 90% of the data in the world was generated in the last two years. Including a machine learning library known as Mahout into the Hadoop ecosystem has enabled us to encounter the challenges of Big Data, especially unstructured data. In the area of machine learning research, the emphasis is given more to choosing or developing an algorithm and conducting experiments based on the algorithm. Such a highly biased view reduces the impact of real-world applications. In this paper, the various applications under the appropriate category of machine learning have been highlighted. This paper makes an effort to bring all the major areas of applications under one umbrella and present a more general and realistic view of real-world applications. Apart from this two application suggestions have been presented forward. The field of machine learning is so vast and ever-growing that it proves to be useful in automating every facet of life.

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 bad board positions are by playing many games against itself. A more formal definition was given by Tom Mitchell as a computer program is said to learn from experience (E) concerning some task (T) and some performance measure (P), if its performance on T, as measured by P, improves with experience E then the program is called a machine learning program. In the checkers playing example, the experience E was the experience of having the program play games against itself. Task T was the task of playing checkers. And the performance measure P was the probability that it won the next game of checkers against some new opponent. In all fields of engineering, there are larger and larger data sets that are being understood using learning algorithms.

Artificial intelligence (AI) and machine learning (ML) are terms that have created a lot of buzz in the technology world and for good reason. They’re helping organizations streamline processes and uncover data to make better business decisions. They’re advancing nearly every industry by helping them work smarter, and they’re becoming essential technologies for businesses to maintain a competitive edge.

These technologies are responsible for capabilities like facial recognition features on smartphones, personalized online shopping experiences, virtual assistants in homes, and even the medical diagnosis of diseases.

This exponential growth is posing problems for organizations. They report that their top challenges with these technologies include a lack of skills, difficulty understanding AI use cases, and concerns with data scope or quality.

AI and ML, which were once the topics of science fiction decades ago, are becoming commonplace in businesses today. And while these technologies are closely related, the differences between them are important. Here’s a closer look into AI and ML, top careers and skills, and how you can break into this booming industry.

Artificial Intelligence vs. Machine Learning: Required Skills

Because artificial intelligence is a catchall term for smart technologies, the necessary skill set is more theoretical than technical. Machine learning professionals, on the other hand, must have a high level of technical expertise.

Artificial Intelligence Skills

People pursuing a career in artificial intelligence must have a foundation in:

1. Algorithms, and techniques for analyzing them
2. Machine learning and how to apply techniques to draw inferences from data
3. The ethical concerns in developing responsible AI technologies
4. Data science
5. Robotics
6. Java programming
7. Programming design
8. Data mining
9. Problem-solving

Machine Learning

People pursuing a career in machine learning must have a foundation in:

• Applied mathematics
• Neural network architectures
• Physics
• Data modeling and evaluation
• Natural language processing
• Programming languages
• Probability and statistics
• Algorithms

CONCLUSION:

Artificial Intelligence and Machine Learning are products of both science and myth. The idea that machines could think and perform tasks just as humans do is thousands of years old. The cognitive truths expressed in AI and Machine Learning systems are not new either. It may be better to view these technologies as the implementation of powerful and long-established cognitive principles through engineering.

We should accept that there is a tendency to approach all important innovations as a Rorschach test upon which we impose anxieties and hopes about what constitutes a good or happy world. But the potential of AI and machine intelligence for good does not lie exclusively, or even primarily, within its technologies. It lies mainly in its users. If we trust (in the main) how our societies are currently being run then we have no reason not to trust ourselves to do good with these technologies. And if we can suspend presentism and accept that ancient stories warning us not to play God with powerful technologies are instructive then we will likely free ourselves from unnecessary anxiety about their use.