We have always tried our computers to go smarter. We always want them to be like us. But it is really difficult for a computer to be a human. The reason is that human brains are complex and much of their operation is still not well understood. Moreover, simple things we do require a huge amount of processing for a computer to produce. What we can do in seconds may take hours for a computer to do. This is where machine learning comes to use. Machine learning is teaching a computer to be a human. In this article, we will be discussing ‘Is Machine Learning Hard As Career In 2021?’
Machine Learning is all about mathematics and statistics, combined with programming to build a perfect software that enables computers to learn and think like humans. While machine learning is an extensively broad and big subject, the concept is pretty much simple. It is not difficult to know the concepts, and most of the machine learning algorithms work on only a selected handful of concepts. The difference between a good ML model from a bad one is the amount of data it uses to build the model.
Machine Learning
The idea of machine learning is to use a computer like a human. Machine learning algorithms are complex programmed concepts that help a computer to think and act like a human.
A human being can recognize a person easily after seeing someone. But the same thing cannot be done on a computer. The reason is that humans use points and measurements to recognize a face. There is a lot of data that we need to recognize a face. We do not understand this because our brain does everything for us in an instant.
Now, imagine the same thing a computer has to do. Since it is a machine, we have to input data into it so that it can work. The amount of data is so much that we often fail to supply them with the required amount of information. This makes machine learning hard.
On the other hand, imagine that you have everything with you. If you have lots of data, building a machine learning algorithm and using it is only a matter of minutes. Some libraries build algorithms in seconds. Machine learning has just become a thing for a teenager!
So, it is the data that makes things complex. Since it is never possible to get such a huge amount of data, we reply to different methods that work the best with the given information.
Machine Learning Techniques
Machine learning is teaching the computer to recognize or predict or detect something in the future. If we want the computer to recognize cats and dogs, it is machine learning. If we want the computer to predict the weather for the next week, it is machine learning. If we want it to detect spam messages automatically, it is machine learning.
Image recognition is always an interesting topic when machine learning is taught. Image recognition works using weights. If we want to distinguish between cats and dogs, we input some details regarding their faces, like the width of the nose or length of ears. There are several points we can enter, and the more, the better. The weights mean which object is more important in recognizing the faces. For example, the length of the ears may be more important and decisive than the length of the nose. In that case, the weight of the ear length is more.
Determining the weights is the job of the algorithm. As a learner, you should focus on the results rather than the build. Building an algorithm is a huge amount of work that is done by separate software engineers.
Widely Used Techniques
Machine Learning uses many algorithms, but for learners, there are a few that are easy to understand. Before we jump into the methods, we need to know the difference between two types of learning methods, supervised and unsupervised learning.
Imagine that you want to predict the price of a house for sale. To do that, you need the market values of houses similar to yours. You input those values as data, build a machine learning model and predict your house value. This uses labeled data to build the model. Labeled data means the data has answers (here, the values of the houses) and these answers are used to predict the answer to your question (your house value). This is called supervised learning.
On the other hand, imagine that you want to detect whether an email is spam or not. In this case, you do not have the answers in your dataset, because all the emails are non-labeled. The model needs user-defined words to detect whether the mail is spam or not. This is unsupervised learning. Unsupervised learning does not have that feedback path that teaches the model to learn from its mistakes. Hence, unsupervised learning is easier to code.
There is also a concept of semi-supervised learning, but those are limited to higher concepts regarding soft computing.
Learning Machine Learning
If you are an intrigued teenager or a newcomer in the world of software, there are simple courses on machine learning that teach the subject in a simplified manner. These courses tell you the science behind the topic and a brief idea about how it is done. If you want to learn more, we come to the types of algorithms mostly used.
There are some simple algorithms, and regression is one of them. Regression is used to predict values. It may be weather, price, or sales count. Then, there is classification, which is used by shopping websites to display recommendations. These are two of the widely used supervised learning models.
Among unsupervised learning models, the clustering model is widely used. The previous example of spam detecting falls in this category. Digital libraries use this method to sort books. It is easier to learn than classification, which is the same thing, but with a labeled dataset.
Lastly, we come into deep learning algorithms. These algorithms use neural networks, which is just how a human brain works. Deep learning algorithms have neurons and units of data that they remember and utilize later on. These methods are very accurate and are mostly used when there is a lot of data. With less amount of data, the neural networks may give inappropriate results.
How to Start Learning?
Before you jump into any full-length courses, first think about what you want to learn. Is it programming? Is it the concept? Or is it just that you are interested in this topic?
You should start with some project-based courses. The elementary courses will give you insights into how things are done. But these courses do not tell you the software details and information.
Machine learning is mostly about statistics and mathematics. Although you do not need much of the higher mathematics for the course, statistics is required. This is needed to know the theory of the machine learning algorithms and understand how it is done and programmed.
If you are interested in a quick hands-on experience, then you need a programming tool. The Linux operating system is the best for machine learning, but other operating systems can be used too. Python is the leading programming language for machine learning, mostly because of its extensive libraries available for the tasks.
Building a machine learning model for your project is a good idea. Even if it is a simple regression prediction model, you can write about it in your resume. You can find machine learning courses in almost every online MOOCS platform, like Coursera, Udemy, etc.
The Scope
The reason machine learning is one of the trending topics today is the vast possible things it can do. Even so, it is still in its budding stage, and lots of people are required to maintain and build such models. Tech giants and famous companies use machine learning today to make their clients’ lives easier. You cannot think of Amazon without its recommendation page.
This makes machine learning enthusiasts earn so much. But it must be remembered that machine learning is a vast and growing topic, and it is important that you keep yourself updated.
Conclusion
Machine learning is a broad subject and it contains many sections. While learning about the models and building them using a library is a task for a young teenager, building those libraries takes an extensive amount of programming and statistics knowledge. For people who are intrigued, it is a major topic that can fascinate with its results. But fetching those results may be a tedious task.
Machine learning mostly depends on the amount of data you have. Even with an elementary-level algorithm, you can achieve great accuracy if you have a sufficient amount of data. However, some models use complex techniques and build models that almost resemble a human brain. It must be remembered that no machine learning method in the world can give you 100% accuracy. It is for prediction, not for complete reliance.
Everything combined, machine learning may be a bit difficult and diabolic for people who do not love statistics and mathematics. They can learn the basic concepts and see how things are done. To be a part of the programming team requires good programming knowledge as well. Python leads in the programming world for machine learning because of its number of available libraries. Julia is another promising programming language fit for coding machine learning algorithms.
