Machine Learning

Being a Human Being, we have lots of ways to learn. One of them is learning from our experiences. Similarly, machines also do learn from their past data. This process is called machine learning. ML is a branch of artificial intelligence where computers learn from data instead of being programmed step by step. Machines learn from different ways such as supervised learning, unsupervised learning, and Reinforcement learning. In other words, ML is like teaching a computer by showing it examples until it can figure out the patterns on its own. 

How Machine Learning Works?

The difference between training a machine and training a human is just that humans can understand the emotion and response with natural intelligence. But machines can’t understand emotions. It gives a response with the patterns based on data. To the humans, we give the information about what is right and wrong, time after time humans learn and implement it in their behavior. Similarly, after giving the data, machines practice using that data until they can make good decisions on their own. In ML, this process involves collecting data, feeding it to a computer model, and letting that model improve over time by spotting patterns. The better the data, the better the results. Just like humans, computers get better with practice and feedback.

More data > Better Model > Higher Accuracy

Types Of Machine Learning

As mentioned before, there are three ways Machine learn:

  1. Supervised Learning: In this learning method, the machines are trained by using labeled data. For example, you show it photos marked “cheese” or “cake,” and it learns to tell them apart.
  2. Unsupervised Learning: Here, the machines are trained by using unlabeled data. In this training process, there’s no fixed output variable. For example, a retail store might use it to group customers by buying habits without knowing those groups beforehand.
  3. Reinforcement Learning: The computer learns by trial and error, like a game. A robot might try walking in different ways until it finds the most effective method.

Applications of Machine Learning

Machine learning is used everyday in our life either knowingly or unknowingly. In schools, healthcare, banks, commerce and e-commerce. In schools, ML is used to analyse the progress report of students, attendance, and fees. Likewise, in healthcare, it helps doctors to figure out the disease of the patients by analysing the past records. In Banks, ML helps to spot unusual transactions and prevent fraud. Lastly, in e-commerce sites, ML helps to suggest the user the product they like. 

Benefits and Challenges

Different from Humans, Machine learning’s biggest advantage is that it can handle tons of data at once. It has low inaccuracy and fast decision making capacity. It helps the businesses function smoothly and smartly. Machine learning can personalize customer experiences, and even reduce human error.

Beside this, while training the Machine, poor quality data can lead to wrong results. There are concerns about privacy when personal information is used for training models. Sometimes, algorithms can be biased if the data they learn from is biased. And in many cases, running these models can be costly in terms of time and computing power.  

Conclusion

Machine learning is growing second by second. It has more potential to grow and become more important in the coming days. It’ll make devices smarter and more efficient. The results will be faster and accurate. Machine learning will change the way we are living now and the way we are working. The data will become more accessible and we can expect machine learning to power innovations in healthcare, banks, school and other everyday tools. Since, ML is the big change in technology, lifestyle, time savior, and it unlocks the possibilities we can never imagine.