Introduction to Machine Learning
I have tried to give a brief introduction to machine learning. Also, I will tell about what is machine learning, the definition of machine learning by Tom Mitchell, and many more...
Introduction to machine learning:
Machine learning is a subfield of artificial intelligence that deals with the learning of machines or models by analyzing the data. It is a concept that deals with making a mimic of the human even better than the human. Here the model will analyze the provided data and train based on the data which is called training data. After the successful training, we will test that model by using the different data which is used to train the model. Also, we will calculate the accuracy of the model, and then we can feed any data and can predict the output of that example data.
We know a lot of data is generated per minute. Every day, about 2.5 quintillion bytes of data is created. So, we can use this data to make things easy. We can implement machine learning models and make many things easier for the human. Also, can come up with the automation of the things. It will help the human to do the work easily with very small effort and save time. I hope you have got the basic idea of machine learning. Now, let’s see what is machine learning?
What is machine learning?
Machine learning is a computer program that enables machines to automatically learn from the data, improve performance from experience, and making a prediction. There are many definitions of machine learning but have the same meaning. Let’s see the definition of machine learning which is proposed by Tom Mitchell.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
The meaning of this definition is like the above sentences that it will learn from the provided data, improve performance from experience, and making decisions or predictions.
Steps to build a model of machine learning:
There are 7 steps to build a machine learning model from scratch. We will see them one by one in short and of course, we are going to use them while developing the machine learning models.
Data collection: To solve a specific problem by using machine learning you need proper data. As I have said above, we need to feed the data first to the model, and then the model will learn. So data is very important to build a machine learning model. Without data, there will be nothing. We can feed the data to the model using files like CSV, xlsx , txt, etc. The data should be related to the problem that you want to solve.
Preparation of data: The data that we have collected may have different attributes with a different format. A machine that is a computer can understand and make a computation on the numerical data only. So, before feeding the data to the model we should convert the data into a proper format so that the machine can understand the data.
Choose the model: There are many algorithms from which we can develop a model. So, we have to choose a proper model from the set of algorithms. There are many algorithms of machine learning. Here I will try to give the names of some algorithms such as linear regression, multiple regression, decision tree algorithm, k nearest neighbors, random forest, CNN, RNN, k-means, etc. Of course, we will see these algorithms also in the upcoming posts.
Training of a model: After accepting the data by the model, it will train using the provided algorithm. It will assign a weight to each feature and come up with the proper weights. by using that weights we can proceed further, but the calculation of these weights is assigned in the training phase.
Evaluation: After the successful training, we will evaluate it, test it using the testing data. We will test the model by providing the data to a model which should be different from the training data. We will test and also calculate the accuracy score.
Parameter Tuning: We can improve the accuracy of the model by doing parameter tuning, and hence we can come up with a better model.
Prediction or Interface: Here, our model is ready to make decisions and make predictions. we can take any data and predict the data.
Advantages of machine learning
Some advantages of machine learning are as follows:
We can save a lot of time by using machine learning models. The work will happen without human interaction and happens automatically. So we can save the time and there is no need to do much work by the human.
Machine learning models can improve their performance continuously from the experience. Hence, we can make the high-performance model and do work much accurately.
There are many other advantages also.
Disadvantages of machine learning
I personally think that automation by machine learning makes things simpler, but this is very harmful to human health. So we can say that this is the main disadvantage of machine learning.
Machine learning models can do the specific work only with high accuracy and it is not flexible like humans. This is our aim to develop the mimic of humans.
Applications of machine learning
I am trying to talk about the applications of machine learning from the following image. Of course, we will talk about each application in upcoming posts.
Conclusion
We have seen a small introduction in this particular post, its definition, the meaning of the definition, steps of the building model, advantages and disadvantages of machine learning, and applications of machine learning at last.
Thank you.
Nice Work. Keep Going
Great. Keep it up.