On Machine Learning

On Machine Learning

Data 

Suppose we delve into a more academic definition of machine learning from Wikipedia. In that case, we find that it is:  Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inferences instead. 

If we analyze the previous definition, we find a few emphasized aspects. It's a scientific study, which means that extensive academic research and effort support its growth and development. Therefore, it's a scientific field. It relies on algorithms and statistical models, which tells us that if we want to master machine learning, we need to develop a robust understanding of algorithms, statistics, and probability. It aims to perform a specific task, which tells us that a machine learning solution has a particular scope, such as prediction and recommendation.

Without explicit instructions, which tells us that we should not program our machine learning solution in an exact way to teach it how to do things. We rely on patterns and inferences, which tells us that our machine learning solution will depend on digesting specific designs from the historical data to learn how to function correctly.

Now let's discuss some interesting real‑world applications of machine learning we see in our daily life.

First, image recognition. Have you ever wondered how your mobile phone can unlock itself, bypassing your face in front of it while not opening itself for other people? It is the image recognition technology that's running that method. By properly feeding the image data to a machine learning algorithm, we can recognize objects. 

Secondly, malware detection where you may have been wondering how antiviruses can effectively learn about new viruses before getting updates. Again, it is the machine learning science that is the engine running this. 

Thirdly, voice recognition. Another well‑known example that utilizes machine learning is voice recognition. Whether it is your mobile phone or home assistance, machine learning makes it possible to identify the user quite effectively to make it a reliable security method. By developing competencies and skills in machine learning, you will develop fantastic solutions that address real‑world problems and bring value to humanity.

Finally, it's worthy of discussing why machine learning is getting such attraction nowadays. Believe it or not, it has been there since the '60s. One can argue that basic statistics and probability are some machine learning. However, there have been some driving forces pushing it in the last few years.

Firstly, problems are becoming highly dimensional. A high‑dimensional deficit is a deficit that has too many attributes, maybe hundreds, which makes it almost impossible to analyze using traditional programming. An example of a high dimensional dataset is the nutritional status, where many features such as immune system status, genetic background, nutrition, operations, drug, and tobacco consumption, among many others. There is a massive data overflow such as RSS feeds, web services, databases, emails, surveys, you name it.

Businesses have recently seen the need to find an efficient way to understand these massive deficits to generate value from it. The computation technology progress and the steady decline of computing and storage resources, for example, in cloud computing, made it efficient to implement machine learning algorithms as machine learning algorithms are generally resourced intensive.

Machine learning research has significantly grown in the last decade. Businesses have started to understand their value; many algorithms were researched and developed and trained skills. So, given all the previous information, we can confidently reassure the statement, data is the new oil. Without data, nothing is here.