Machine Learning In 4 Minutes

A young mind will be drawn to the two words as they literally are. A novice will more commonly think of a machine in this context as mechanical, electrical, or electronic equipment that performs a task. By adopting this viewpoint, individuals may attempt to comprehend machine learning in terms of a physical mechanical mechanism. To grasp this concept, there need to be some adjustments.

Putting both terms together means the computer is the machine that is being taught to become experienced and knowledgeable, hence capable of doing things on its own.

What do we mean by "on its own"? Unlike traditional programming in androids, desktops, and the like, the programmer does not build his codes hardwired. Instead of directly writing blocks of conditionals, the machine is taught from past results and allowed to freely come up with results from a wider domain or range. It is on this capability that we can say a system is intelligent or smart.

Machine learning?(ML) is a field in data science that clearly explains artificial intelligence. It is devoted to understanding and building methods that can learn from previous data to perform some set of future tasks. It typically focuses on developing algorithms based on previous data gathered from an area of life, such as education, medicine, security, agriculture, biology, etc.

This huge data is gathered from past activities and applied in machine learning to make models that can carry out activities(tasks). The practice of building these models and introducing them into different systems is called MLOps. The activities of a model could be grouping characters or features together(typically called clustering), making future predictions, or looking for paths to make the most effective decision among many possible decisions(optimization). The computer does all this without being programmed directly to do so but rather through the past data (dataset).

In everyday applications, the tasks of machine learning algorithms can be applied to email filtering(in marketing), speech recognition(e.g., personal assistants), commodity price prediction(in business sales), etc.

Types of machine learning

As discussed, the usage of machine learning is based on 3 main algorithms, which are seen in all 3 types. They include?supervised learning,?unsupervised learning,?and?reinforced learning.

1. Supervised machine learning?is a model that works on problems where both the inputs and the desired outputs are known. Let me explain what this means. Inputs are like the conditions that need to happen in a problem domain, while outputs are what we want to know from the conditions. For instance, the inputs in your rain prediction app may be the month of the year, the date, the amount of previous rainfall, the location, the current temperature, etc., while the output may be whether it will rain or not(rain/not rain). In this problem, we know the inputs and even though we don’t know whether it will rain or not rain, we know our result domain(rain or not rain). Nothing outside of this. Since the computer has previous data on how it has happened in the past, it can follow the supervision of the past inputs and outputs(i.e., whether it rained or not) and learn to say what is likely to happen today.
2. Unsupervised learning: It's okay if you are thinking unsupervised learning is the opposite of supervised learning. Though it doesn’t clearly oppose it, unsupervised learning has inputs but we don’t have an expectation like whether it will rain or not. We only use the inputs to look for information that may be hidden in the data. This may include grouping the data according to their similarity, called?clustering.?This means that unsupervised learning algorithms are efficient in identifying similarities and behaviours in the dataset and producing outputs based on their experience with the data. An instance of supervised learning is clustering or grouping students from an academic dataset to see those who prefer calculative courses and those who prefer theory or practical courses.
3. Reinforcement learning?is quite out of the scope of explaining the previous two algorithms. It focuses on the problem domain of machine learning, where the problem is on how the most suitable action needs to be taken in an environment so as to maximize benefits and minimize some expenses. It is wider and hence more complicated and is used in different areas of endeavour. Reinforced learning is quite commonly used in developing games, making timetable systems, operations research, etc. Reinforcement learning algorithms are used sometimes when an exact approach seems not to work. Hence, it doesn’t assume knowledge of an exact mathematical model of the development process. With the promises reinforcement learning holds, there are more chances of developing smart robots and advanced AI.

In conclusion, machine learning is a fast-growing area in the sciences since the associated tasks with computers are continually becoming more complex, and also huge amounts of data are being gathered in different areas of life. Wherever there is a huge record of data, there is a huge possibility of utilizing this data in machine learning.