Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.

In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.

As a Human: Let’s suppose one day you went for shopping mangoes. The vendor had a cart full of mangoes from where you could handpick the mangoes, get them weighed(doing weight) and pay according to the rate fixed per Kg. 

Task: How will you choose the best mangoes?

What if you have to write a code for it?

As a Human Written Code: Now, imagine you were asked to write a computer program to choose your mangoes. You might write the following rules/algorithm:

1. if is bright yellow and size is big and sold by: mango is sweet.
2. if (soft): mango is juicy

You would use these rules to choose the mangoes.

Conclusion as a human:

But every time you make a new observation from your experiments, you have to modify the list of rules manually.

You have to understand the details of all the factors affecting the quality of mangoes. If the problem gets complicated enough, it might get difficult for you to make accurate rules by hand that covers all possible types of mangoes. This will take a lot of research and effort and not everyone has this amount of time.

This is where Machine Learning comes into the picture

Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the algorithm/machine builds the logic based on the given data.

How does Machine Learning work?

A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Need for Machine Learning

The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us.

We can train machine learning algorithms by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money.

The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly.

machine learning can be classified into three types:

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

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