Machine Learning is the science of programming computer systems so they can learn from data. It is a branch of artificial intelligence that aims at enabling machines to perform their jobs skillfully by using intelligent software. ** machine learning ****Prerequisites:** It is a mixture of mathematical optimization and statistics, each tutorial disciplines in their very own right. Machine learning is part of computer science, and therefore its practitioners are extremely skilled computer programmers.

** **In case you simply Download a copy of W3schools, your computer has a lot more data, however it is not suddenly better at any task. Thus, it’s **not Machine Learning**.

Machine learning is presently driving one thing of a recognition wave. Machine Learning is a natural outgrowth of the intersection of Computer Science and Statistics

** **machine learning prerequisites

- Intermediate knowledge of Programming languages like Python & Java is required, if you don’t know how to get started learning Python than go ahead and read the article featuring best learning resources for python.
- you should have a reasonable understanding of college-level math i.e – calculus, linear algebra, probabilities, and statistics
- You should be familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib.

**Machine Learning is very useful for following tasks**

- Issues for which existing solutions require numerous hand-tuning or long lists of guidelines: one Machine Learning algorithm can often simplify code and perform better.
- Advanced issues for which there is no good solution in any respect using a conventional strategy: the most effective Machine Learning techniques can discover a answer.
- Fluctuating environments: a Machine Learning system can adapt to new data.
- Getting insights about complicated issues and enormous amounts of information.

Machine Learning programs could be **categorized** according to the amount and type of supervision they get throughout training. There are **4 main categories**:**1. supervised learning ,****2. unsupervised learning , ****3. semi-supervised learning, ****4. Reinforcement Learning**

#### Conclusion

Machine learning algorithms are driven by mathematics and statistics, and the algorithms that uncover patterns, correlations, and anomalies within the information differ broadly in complexity. At its core, machine learning is a set of mathematical methods, applied on computer programs, that permits a course of of data mining, pattern discovery, and drawing inferences from information.