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Posted 26 Jul 2018
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What is Machine Learning?

, 26 Jul 2018
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Machine learning and the types of machine learning, and how we teach the machine to perform different actions


Artificial Intelligence, Machine Learning is the most trending technologies these days. Most of the time, we confuse about the things that they are so much critical but actually, they’re not. Artificial Intelligence teaches computers to behave like humans, to think and to give responses like a human, to perform the actions like humans perform.

What is Machine Learning

As the name suggests,

Machine Learning

(Machine is Learning)

This is the technique through which we teach machines about things. It is the branch of Artificial Intelligence and I would say it is the foundation of Artificial Intelligence. Here, we train our machines using data. If you take a look into it, you’ll see that it is something like Data Mining. Actually, the concept behind it, Machine Learning and Data Mining are both data oriented. We work on data in both situations. Actually, in data sciences or big data, we analyze the data and make the statistics of it and we work on how we can maintain our data, how we can conclude the results and make a summary of it instead of maintaining the complete comprehensive bulk of data. But in Machine Learning, we learn the machines to make decisions about things. We teach the machine with different data sets and then we check the machine for some situations that what kind of results we get from this unknown scenario. We also use this trained model for prediction in new scenarios.

We learn the machine with our historical data, observations, and experiments. And then we predict from the machine from these learnings and take the response.

As I already said, Machine Learning is closely related to data mining and statistics.

Data Mining:       Concerned with Analytics of Data.

Statistics:             Concerned with Prediction-Making/Probability

Why We Need Machine Learning?

In this era, we’re using wireless communication, internet, using social media, driving cars anything we’re doing right now is actually generating the data at the backend. If you're surprised about how our cars are generating the data, remember that every car has a small computer inside which controls your vehicle completely, i.e., When which component needed the current, when the specific component needs to start or switch. In this way, we’re generating TB (terabytes) of data.

But this data is also important to conclude the results. Let’s take an example and try to understand the concept clearly, let’s suppose a person is living in a town and he goes to a shopping mall and buys something. We’ve many kinds of items of a single product. When he buys something, now we can generate the pattern of the things he bought. In the same way, we can generate the selling purchasing patterns of things for different people. Now you might be thinking about a random person coming and buying something and then he never come again, but we have the pattern of things as well there. With the help of this pattern, we can make the decision of the things people most like and when they come again in the mall, they don't need to go inside and find out the things. They will get the things just at the entrance, this is how we attract the customer with Machine learning.

How Machine Learns?

Actually, machines learn through the patterns of data. Let’s start with the data sets of data, the input we give to the machine called X and the response we get is Y. Here, we’ve 3 types of learning:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

In supervised learning, we know about the different cases (inputs) and we know the labels (output) of these cases. And here, we already know about the ground truths, so here we just focus on the function (operation) because it is the main and most important thing here.

Here, we just create the function to get the output of the inputs. And we try to create the function which processes the data and try to give the accurate outputs (Y) in most of the scenarios.

Because we’ve started with known values for our inputs, we can validate the model and make it even better.

And now we’ve taught our machine with different data sets. Now it is time to check it in the unknown case and generate the value.


Let’s suppose you’ve provided the machine a data set of some kind of data and now you trained the model according to this data set. Now the result that comes to you from this model is on the basis of this knowledge set you’ve provided. But let’s suppose if you delete an existing item in this knowledge set or you update something then don’t expect that you’re getting the results according to this new modification you’ve made in the dataset. Now, this trained model is a waste for you, you’ve to train the model according to your knowledge base input items from the start to get the results accordingly.

How Supervised Learning Works?

Let’s take an example here of iPhone. Let’s suppose different customers purchase the iPhone in different years and its price gradually increases.

Image Credit Goes To TowardsDataScience

Here is the general image for any model. Here, we can see how many cases we have here. Let’s suppose different customers purchase the iPhone with different prizes and we know each year, its price increases. We already know that the thing which is independent comes horizontally in the graph and vertical represents the dependent thing. So prize works vertically in the graph. Now draw a line which touches the maximum points. This straight line is the supervised learning of your model. Your model can take a decision if the next version of iPhone comes in the next year how much the prize it will be. This is how we’re predicting the values of the things. Although it is not so accurate, it is approximately near to the things.

Unsupervised Learning

It is quite different from supervised learning. Here, we don’t know about the labels (output) of different cases. And here, we train the model with patterns by finding similarities. And then, these patterns become the cluster.

Cluster = Collection of Similar Patterns of Data

And then, this cluster is used to analyze and to process the data.

In unsupervised learning, we really don’t know about the output whether it is right or wrong. So here in this scenario, the system recognizes the pattern and tries to conclude the results until we get the nearly right value.

Example of Machine Learning

Machine Learning basically is a Problem-solving tool. Like if you play the chess game on a computer or even in a mobile phone, then the computer knows about different steps and what step he would perform after you, the computer knows. Here, we’ve trained our machine to play chess with a user.

Reinforcement Learning

It is like reward-based learning. The example of reward based is suppose parents will give you a reward for the completion of a specific task. So here, you know you’ve to complete this task and how much it is necessary for you. Here, the developer decides himself what reward he’ll give on the completion of this task.

It is also Feedback oriented learning. Now, you’re doing some tasks and on the basis of these tasks, you’re taking feedback. And if the feedback is positive, then it means you’re doing right and you can improve your work on your own. And if the feedback is negative, then you know as well what was wrong and how to do it correctly. And feedback comes from the environment where it is working.

It makes the system more optimal than the unsupervised scenario. Because here, we have some clues like rewards or good feedback to make our system more efficient.

Steps in Machine Learning

There are some key point steps of Machine Learning. How we start and learn the machine.

  • Collect Data

As we already know, Machine Learning is data oriented. We need data to teach our system for future predictions.

  • Prepare the Input Data

Now you’ve downloaded the data but when you’re feeding the data, then we need to make sure the particular order of the data to make it meaningful for our Machine Learning Tool to process it, i.e., .csv file (comma separated value). This is the best format of the file to process the data. Because comma separated values help a lot in clustering.

  • Analyzing Data

Now you’re looking at the patterns in the data to process it in a better way. We’re checking the outliers (scope & boundaries) of the data. And we’re also checking the novelty (specification) of the data.

  • Train Model

This is the main part of the Machine Learning when we’re developing the algorithm where are structuring the complete system with coding to process the input and give back the output.

  • Test the Model

Here you’re checking the values you’re getting from the system whether it matches your required outcome or not.

  • Deploy it in the Application

Let’s discuss an example of Autonomous Cars (automatic cars) which doesn’t have human intervention, which runs its own. The first step is collect the data, and you’ve to collect many kinds of data. You’re driving the car which runs its own then it should know the road signs, it should have the knowledge of traffic signals, when the people crossing the road, then it can make the decision to stop or run in different situations. So we need a collection of images of these different situations, it is our Collect Data Module.

Now we’ve to make the particular format of data (images) like CSV file where we store the path of the file, the dimensions of the file. It makes our system processing efficiently. This is what we called Prepare the data.

And then, it makes the patterns for different traffic signals (red, green, blue), for different signboards of traffic and for its environment like car or people running around it. And then, it decides the outlier of these objects whether it is static (stopped) or dynamic (running state). And then, it can make the decision to stop or to move to the side of another object.

These are all the decisions that are obviously dependent upon the train the model, what code we write to develop our model. This is what we say training the model and then we test it and then we deploy it in our real-world applications.

Applications of Machine Learning

Machine Learning is widely used today in our applications.

  • You might use the snapchat or Instagram app where you can apply the different animals body parts on your face like ears, nose, tongue, etc. These different organs are placed at the exact right spot in the image, this is an application of Machine Learning.
  • Google is widely used AI, ML. Google Lens is an application if you scan anything through Google lens, then it can tell the properties features of this specific thing.
  • Google Maps is also using Machine Learning. Like if you’re watching any department store on the map, then sometimes it is telling you how much it is branded, how expensive it is.


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


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