Machine Learning Algorithms – Basic Concepts

Machine Learning is getting computer to program themselves without human intervention. If only automation is so, then machine learning definitely is automating the process of automated direction. Programming is merely automation, then simply automating the entire process would be the bottleneck. Writing programs is still the bottleneck, do not have enough quality developers. Let the data take the burden rather than humans.

When humans are involved in the process, there are some upsides and some downsides. Human interaction is a good way to make changes, see what the user wants to do and get rid of wrong things. If you are writing a program, you would obviously need a human being to modify the basic concepts if you change their usage. It will cause a great disturbance in the basic algorithm. So what about machine learning?

It does not have this problem as it uses very precise hypothesis space. The hypothesis space of a machine learning algorithm is simply the set of all possible inputs given a particular output. You see, in a traditional programming, you would have to convert your data into a series of instructions and call them individually to execute the desired function. For example, if you want to calculate the value of a number based on some statistical data, then you have to convert that data series into a series of numbers and assign a weight to them based on the mathematical algorithm. This is called the numerical data transformation.

In machine learning algorithms, all these problems are taken care of by the concept of hypothesis space. Hypothesis space basically indicates the set of all possible inputs a system can use to calculate a particular output. All the usual functions like logistic, geometric, exponential, binomial etc… are transformed into hypothesis space and become functions of a single algorithm. In a way, it can be said that all the fundamental machine learning algorithm operations are made simpler and more generic.

Therefore, the main challenge with implementing machine learning algorithms in traditional programming has always been their efficiency and robustness. Traditional computers, as we know, are highly inefficient when it comes to storing and running large programs. They also have limited memory capacity and processing speed and this limits the amount of results a computer can return at one time. On the other hand, machine learning algorithms are much more efficient as they can run multiple programs and can return results for multiple inputs. They can even store pre-processed inputs and run them multiple times until they find the best result.

Furthermore, a big advantage of machine learning algorithms over traditional statistical learning methods is that they do not rely on any kind of prior knowledge. You don’t need to be a genius to implement an artificial intelligence system. You just have to know how to operate the program and how it works. With the statistical learning methods, on the other hand, you need to be intimately knowledgeable about the statistical concepts used in the scientific world in order to understand the output. Of course, there are specialists in the scientific community who are in charge of developing these systems but they are too few and far between to be of much help to the average person.

In the machine learning process, however, it is quite easy to train an algorithm to make accurate and reliable predictions. First, it needs to learn from its past mistakes. It then needs to predict the target function in future by feeding in new data. Unlike the standard statistical method, which needs the trained machine to learn from actual or old data, the machine learning algorithm can learn continuously from its own mistakes. It learns from its own past mistakes and makes predictions based on this data.

The biggest weakness of machine learning algorithms lies in their simplicity. Although these algorithms can process large amount of data and can even tell the future trend, they still lack the ability to perform independent verification or validation. For instance, in the standard statistical method the target function must be specified and tested repeatedly, which requires the programmer to actually use the numbers in calculations. Because of the incompleteness of the statistical test, the programmer needs to test the system using different inputs. This additional step provides extra assurance that the target function is actually determined.