Machine Learning: Everything You Need to Know

To understand machine learning we need to have a better understanding of artificial intelligence. In simple words, if we are required to describe Artificial Intelligence it can be termed as intelligence which a computer possesses or displays to run a certain program. So, if we have to define what machine learning is it would be fair to conclude that without Artificial Intelligence machine learning can’t exist.

Machine learning can be described as the system’s ability to reprogram itself or in simple words, the system can detect its shortcomings and without any external support, it counters the problem or the task in hand. The basic idea behind machine learning is the development of programs that can help the system in learning better and counter problems effectively.

Major Aim of Machine Learning:

As we know the process of learning primarily consists of steps like observations and collecting data. Now, this data is used and the system is instructed in some way based on the data that was provided to the system in the first place. The rationale behind machine learning is to allow the system to solve problems through analytical abilities and that too should happen without any assistance being provided to the system.

Methods of Machine Learning:

Usually, machine learning methods are classified into two categories. But given the algorithms, they can broadly be classified into four categories. These are listed as follows:

  • Supervised machine learning
  • Unsupervised machine learning
  • Semi-supervised machine learning
  • Reinforcement machine learning

We will be elaborating on all of these types one by one.

1. Supervised Machine Learning algorithm:

This type of algorithm can analyze and learn from the data provided to the system in the past and convert it into newly compiled data to predict the events that can occur in the future. In much simpler words the system analyzes the previous data and then it concludes about the output that a specific program will produce. When the system is trained to, only then it can create targets that can act as new input data.

Additionally, the system can also compare its data with the other outputs as well as inputs. This will help the system to better identify and troubleshoot the problems in hand. The task performance can also be enhanced through this method because the comparisons lead to easy recognition of errors thus it can either be avoided or is solved by the system.

2. Unsupervised Machine Learning:

An Unsupervised machine algorithm trains the system into analyzing the data that is not identified. This doesn’t necessarily mean that the data would be classified but it can simply be implied that the data is just not supported. To analyze and conclude this kind of data unsupervised machine learning algorithm is used. However, the results might not be accurate but this helps the system in identifying the data that is not being labeled. Hidden structures can also be identified through this algorithm and that is something that a supervised algorithm can’t perform. This algorithm also utilizes a unique set of approaches for better learning of the system which is called deep learning.

Now this deep learning is used to review all the unlabeled data and that results in outputs. Because of the complexity of these algorithms, they are also known as neural networks. These are mostly used in image recognition systems as well as speech-to-text systems. When the system is fully trained it will utilize all of its previous data to form a new one. Thus, this results in their excess demands as the information is in massive numbers and only these algorithms can process them.

3. Semi-supervised Algorithm:

This specific type of algorithm is better than both supervised and unsupervised algorithms. The reason behind this is the fact that this algorithm can work on both labeled and unlabeled files. However, the labeled files are lesser in number than the unlabeled ones.

The learning based on these algorithms is usually fast and provides more doors to the system to improve the learning ability of that specific system. The algorithm is used conditionally and is most relied on the cases of labeled data that require resources to be processed. As for unidentified data, they don’t require extra resources to be spent on them.

4. Reinforcement Machine Learning Algorithms:

This algorithm is known for interacting with the environment to execute actions that then decide either the program results in an error or is fruitful in some way. Trial and error are the most distinctive properties of this algorithm. To achieve its true potential, this algorithm allows software agents to determine the behavior of a certain context. Feedback is required in this algorithm which is given to the software agent to be utilized. That specific action is termed a reinforcement signal.

Also read: Machine Learning Changes the Way Machines Work with You

Examples of Machine Learning:

There are tons of examples from the internet that can signify the importance of machine learning. Take an example of the social app which is used by almost everyone in the world, Facebook. Facebook also works on machine learning and has specific algorithms that are purposely designed for a certain news feed.

Since every news feed shows a different kind of data, the algorithm helps the system in learning that data to predict what kind of posts a person might want to see on their news feed. Not only this, but the ads on Facebook are also working on the algorithms. If a person likes a certain page of fitness products, Facebook will aid these ads to reach the audience. In case if you report a certain post of certain somebody on Facebook the algorithm comes into play and you will see less of their post on your news feed.

But these algorithms are not just stopping at the Social sites. They are vastly used as a marketing strategy. Customer relationship management can analyze an email and can pin the emails as important so that the sales team can assist them. This creates a better relationship between the consumer and the sales company.


These algorithms are also being tested in self-driving cars. This can help reduce accidents as well. Virtual assistants also work in the same way as they analyze a person’s speech to carry out their tasks like scheduling their day and searching on Google for them. The virtual assistants can also book rooms or a flight as per the instruction provided to them.

Also, the algorithms can work in an organization and can evaluate an employee’s working and will be able to reward the client according to the company’s policy.

Predictive programming is becoming a norm these days whether it is the market or the facility where researches are being made. The reason behind this is that the world needs technology that requires no assistance and can perform tasks on its own. Many companies are following this trend, to make their systems more advanced and to excel in the war of AI.

Future Prospects of Machine Learning:

Since the beginning of the internet, machine learning was around in some way. But with the dawn of artificial intelligence, machine learning is getting more attention than it used to get.

The models that have full command of deep learning are now termed as the most advanced AI applications. There is a sense of great competition in the companies that provide platforms for machine learning. This is because these companies are advanced and are known worldwide for their technological revolution.

Many vendors provide a platform for machine learning but in our opinion, the top ones are Google, Amazon, Microsoft, and IBM. With the dawn of the tech revolution, these algorithms are also improving and creating doorways for unlimited potential. There is also some competition between AI and machine learning even though both require each other to work efficiently.

To develop general applications, researches are being made on deep learning and AI to get positive results. However, this requires extensive training on the platforms and researchers are working on making models that are flexible in nature which can provide more room for system learning.

Data Security:

Malware is the real problem and is evolving as the systems are evolving. But so are the algorithms. With the algorithms that are evolving, they can easily diagnose errors within systems and can eliminate them easily. With the new bugs in the systems, there are always algorithms that can counter those bugs and thus making AI and machine learning more advanced than ever.

Financial Trading:

Trading companies like bitcoins and forex are using algorithms to predict the ups and downs of the market thus predicting the losses and profits insight.

WeblineIndia, a leading machine learning services provider guarantees you the best experience with its programmers in the field of machine learning. The company works on algorithms that can make your system more advanced and will provide your systems with room to specialize in tasks that you want them to perform.

I am a Business Growth Strategist at a Leading Software Development Company. I have experience in developing and executing digital strategies for large global brands in a variety of business verticals. Apart from working on a long-lasting relationship with customers and boost business revenue, I am also interested in sharing my knowledge on various technologies and its influence on businesses through effective blog posts and article writing.