Machine Learning can be a great choice if you are looking for a solution to your problems. Machine learning can figure out how to perform important tasks by generalizing from the examples. This process is often cost-effective and less time consuming as compared to manual programming. As more and more data is made available to the machine learning algorithms, more complex problems can be solved.

However, developing successful machine learning applications requires skills and knowledge that is hard to find in the books. Here, in this article, we will be discussing that is the machine learning the right choice for you if you want to tackle a real-life problem?

All the machine learning algorithms comprise the combination of 3 components. The components are:

**Representation**: A classifier can be represented in some formal language that a computer can handle. But choosing the representation is difficult and a set of classifiers have to be chosen so that it can learn. If a classifier is not in the hypothesis space, it cannot be learned. A viable question also arrives at how we can represent the input and what features we can use.

**Evaluation**: An evaluation function is also needed to distinguish the good classifiers from the bad ones. The evaluation function used internally by the algorithm can differ from the external one.

**Optimization**: in the end, we need a method to search among the classifiers in the language for the highest scoring. The choice of optimization is key to efficiency. It can also help to determine if the classified product has more than one optimum.

You might be curious to know how to apply machine learning, AI, big data to your domain and solve the problems. So how you can decide which problems can be solved by the machine learning applications. To decide this, you must think about the problem to be solved. You can start by distinguishing between an automation problem or the real-life problem.

Automating the tasks without learning is appropriate when the problem is relatively simple. These tasks have a clear and predefined step that is executed by humans and can be solved by the machines. This type of automation is taking place in businesses for a long time now. For example, hedge funds can filter out the bad data in the form of a negative value for trading volume, but it can’t be negative.

For the problems that are more complex, the standard automation is not enough. The algorithms require learning from the data and in this case, machine learning is the right choice. Machine learning can be seen as a set of statistical methods to find patterns in the data. These methods have the capability to determine how the features of data are related to the outcomes that you are interested in.

Machine learning has the capability to solve real-life problems. These are the problems that require prediction and not just the casual interference, the problems that are self-contained or the problems that have less interference from outside. For example, researchers at the University of machine learning algorithm predicted mortality rates from pneumonia.

So what are the business problems where machine learning can be the right choice for you? The problems that require predictions means that you are interested in knowing how data is related to each other. The statistical method of problem-solving has no space for intuition, theory and domain knowledge. Some of the examples of good machine learning algorithm are predicting the likelihood that a user will click on certain kind of advertisement or evaluate that if a certain type of text is similar to previous text you have seen.

The examples where machine learning cannot be implemented are predicting new product line or predicting future sales from the past data when a new competitor has just entered the market. Once you are clear in your mind that your problem is suitable for machine learning, the next step is to evaluate whether you have the appropriate data to solve the problem. The data might be previous data from the business or the data from external sources.

For example, you have determined that your problem can be solved by machine learning and you have the right data for the problem. The last step of the process is the intuition check. With more understanding of the concept comes more realistic expectations. Once you ask enough questions and receive enough answers, you get a better understanding of how machine learning works. The machine learning can give results that you cannot anticipate.

Therefore, the last step is to set the margin of error. You have to decide what to what extent you can allow statistical errors in your process. Is your problem the kind of problem where 80% accuracy is enough. Can you deal with a 10% error, 5%, 1%? Are there certain kinds of errors that should never occur? You should be clear about your expectations and needs.

Machine learning is a technology that has the capability to solve real-life problems. You just have to identify the problems where machine learning can be applied. Finally, developing the right solution to a real-life problem is just an applied mathematics problem. The problem requires awareness of business demands, rules and regulations. In solving the real-life problem, being able to combine these is crucial and those who can do this can create the most valuable results.

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