As you know we are living in a world of humans and machines. humans have been evolving and learning from the past experience for millions of years, on the other hand, the era of machines and robots have just begun. in today's void, these machines or the robots are like they need to be programmed before they actually follow your instructions but what if the Machine started to learn on their own and this is where machine learning comes into the picture.
Machine learning
Machine learning is the core of many futuristic technological advancements in our world. today you can see various examples or implementation of machine learning around us, such as Tesla's self-driving car, Apple Siri Sophia AI robot, and many more Are there.
What Exactly is Machine Learning?
Machine learning is a subfield of artificial intelligence, that focuses on the design of a system that can learn from, and make decisions and predictions based on the experience which is data. in the case of machines machine learning enables the computer to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task. these programs are designed for learning and improving over time to time. when exposed to new data.
Let's move on and discuss one of the biggest confusion of the people in the world they think that all the three of them.
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| Ai Vs ML Vs Deep Learning |
AI the Machine learning and the Deep learning all are the same you know what they are wrong. let me clarify things for you artificial intelligence is a broader concept of Machines being able to carry out tasks, in a smarter way. it covers anything which enables the computer to behave as humans. Think of a famous Turing test to determine whether a computer is capable of thinking like a human being or not.
If you are talking to Siri or Alexa on your phone and you get an answer, you are already very close to it, so this was about the artificial intelligence.
Now coming to the machine learning part, so as I already said Machine Learning is a subset of a current Application of AI. it is based on the idea that we should be able to give machine the access to data and let them learn from themselves.
Machine learning is a subset of artificial intelligence that deals with the extraction of pattern from data set. this means that the machine can not only find the rules for optimal behavior but also can adapt to the changes In the world. Many of the Algorithms involved have been known for decades centuries, even thanks to the advances.
In the computer science and parallel computing they can now scale up to massive data volumes. so this was about the machine learning part.
Deep Learning:
Deep learning is a subset of machine learning where similar machine learning algorithm are used to train deep neural networks. so as to achieve better accuracy in those cases where former was not performing up to the map. Machine learning, AI and deep learning all three are different.
Machine learning work?
One of the approaches is where the machine learning algorithm is strained using a label or unlabeled training data set to produce a model. new input data is introduced to the machine learning algorithm and it make prediction based on the model.
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| Working of Machine learning |
The prediction is evaluated for accuracy and if the accuracy is acceptable the machine learning algorithm is deployed.
now if the accuracy is not acceptable the machine learning algorithm is strained again and again with an argument a training data set. this was just in high-level example as there are many more factor and other steps involved in it.
Sub categories of Machine Learning into three different types-
let's see what each of them are how they work and how each of them is used in the field of Banking, Healthcare, Retail and Other domains.
What is supervised learning?
A mathematical definition of supervised learning is. where you have input variables X and an output variable Y and you use an algorithm to learn the mapping function from the input to the output. that is y equal FX the goal is to approximate the mapping function, So a lot whenever you have a new input data X you could predict the output variable.That is why for that data.
Let me simplify the definition of supervised learning. so we can rephrase the understanding of the mathematical definition as a machine learning method. where each instances of a training data set is composed of different input attribute and an expected output.
The input attributes of a training data set can be of any Kind of Data it can be a pixel of image, it can be a value of a database row or it can even be a audio frequency histogram.
For each input instance and expected output values associated the value can be discrete, representing a category or can be a real or continuous value.
In either case the algorithm learns the input pattern that generates the expected output.
now once the algorithm is trained it can be used to predict the correct output of a never seen input.
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| Supervised Machine Learning |
In this image you can see that we are feeding raw inputs as image of Apple to the algorithm, as a part of the algorithm, We have a supervisor. who keeps on correcting the machine or who keeps on training the machine.
It keeps on telling him that yes it is a Apple no, it is not an apple. Things like that so this process keeps on repeating until we get a final train model. Once the model is ready it can easily predict the correct output of a never seen input.
In the image of a green apple to the machine and the machine can easily identify it as yes it is an apple and it is giving the correct result.
Let's discuss another example of it. so in this below image shows an example of a supervised learning process used to produce a model which is capable of recognizing the Ducks.
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| Supervised Machine Learning |
In the image the Teaining data set is composed of label picture of ducks and non ducks. The result of supervised learning process is a predictor model. Which is capable of associating a label duck or not duck to the new image presented to the model. Now once trained the resulting predictor model can be deployed to the production environment.You can see a mobile app for example once deployed it is ready to recognize the new pictures.
now you might be wondering why this category of machine learning is named as supervised learning? well it is called as supervised learning because the process of an algorithm learning from the training data set can be thought of as a teacher. Supervising the learning process.
if we know the correct answers Gorem iteratively makes while predicting on the Training data and is corrected by the teacher. the learning stops when the algorithm achieves an acceptable level of performance.
Popular supervised learning algorith Example:
Now let's see some of the popular use cases of supervised learning,
so we have Co Donna so Co Donna or any other speech automation in your mobile.phone trains using your voice and once trained, it started working based on that training. this is an application of supervised learning.
Suppose you are telling ok Google call Sam or you say "hey Siri call Sam" you get an answer to it and the action is performed and automatically a call goes to Sam. so these are just an example of supervised learning.
A weather app based on some of the prior knowledge like when it is sunny. the temperature is higher when it is cloudy. humidity is higher any kind of that they predict the parameters for a given time so this is also an example of supervised learning.
we are feeding the data to the machine and telling that whenever it is sunny the temperature should be higher. whenever it is cloudy the humidity should be higher. so it's an example of supervised learning.
Biometric attendance where you train the Machine and after couple of inputs of your biometric identity. Beat your thumb your iris or your earlobe or anything once trained the machine can validate your future input and can identify you.
Use of Supervised Machine Learning:
In the field of banking sector:
In banking sectors. supervised learning is used to predict the creditworthiness of a credit cardholder by building a machine learning model, to look for faulty attributes by providing it with a data on deliquent and non deliquent customers.
In The healthcare sector:
In the healthcare sector it is used to predict the patient's readmission rates by building a regression model by providing data on the patient's treatment administration and readmissions, to show variables that best correlate with readmission.
In The retail sector:
Retail sector it is used to analyze a product that a customer buy together. At as this by building a supervised model to identify frequent itemsets and association rule from the transactional data.
Next Type of machine learning is
2. What is Unsupervised learning?
3. what is reinforcement learning?
Click the question fore more........
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