This machine learning tutorial Provides fundamental and intermediate ideas of Machine Learning. It is designed for college students and running specialists who're whole beginners.At the end of this tutorial, you will not be an expert in machine learning, but you will Be able to build machine learning models that can perform complex tasks such as predicting house prices or recognizing iris species by dimensions Its petals and sepals are elongated. If you are not complete beginner and a little familiar with machine learning, suggest starting with subtopic eight i.e. Types of Machine Learning.
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Before jumping into the tutorial, you should be familiar with Pandas and NumPy. It is important to understand the implementation part. There are no prerequisites for understanding the theory. Here are the sub-topics that we are going to discuss in this tutorial:
What is Machine Learning?
Arthur Samuel coined the term machine learning in the year 1959.He was a pioneer in artificial intelligence and computer gaming and defined machine learning as a field of study that gives computers the ability to learn without being explicitly programmed".
In simple words, Machine Learning is an application of Artificial Intelligence that enables a program to learn from experiences and improve itself at a task without being explicitly programmed For example, how would you write a program that can identify fruits based on their various properties, such as color, size, shape, or any other property?
One way is to hardcode everything, create some rules, and use them to identify fruits. This may seem like the only way and work but no one can ever make an absolute rule that applies to all cases. This problem can be easily solved by using machine learning without any rules which makes it more robust and practical. In the coming sections you will see how we will use machine learning to do this.
Thus, we can say that machine learning is the study of making machines more human in their behavior and decision making, which gives them the ability to learn at least. human intervention, i.e., no explicit programming. Now the question arises that how one can gain experience in a program and from where does he/she learn? The answer is data. Data is also called the fuel for machine learning and we can safely say that without data there is no machine learning.
You might be thinking that the term machine learning was introduced in 1959 which is a long time ago, so why not mention it till recent years? You would like to note that machine learning requires a huge computational power, a lot of data and tools that are capable of storing such huge data. We have recently reached the point where we now have all these requirements and can practice machine learning.
How is it different from traditional programming?
Are you wondering how machine learning differs from traditional programming? Well, in traditional programming, we would feed the input data and a well written and tested program into the machine to generate the output. When it comes to ML, the input data along with the outputs associated with the data is fed into the machine during the learning phase, and it generatesagenerates a program for itself. To understand this better, look at the illustration below:
Don't worry if you don't understand it completely, you will get better understanding in the coming sections. You might want to come back to this figure once we discuss steps involved in machine learning to clear all your doubts.
Why do we need Machine Learning?
Machine learning needs full attention today. Machine learning can automate many tasks especially those that humans can do with their innate intelligence. This intelligence can be replicated in machines only with the help of machine learning.
With the help of machine learning, businesses can automate routine tasks. It also helps in building models automatically and rapidly for data analysis. Various industries depend on vast amounts of data to optimize their operations and make intelligent decisions. Machine learning helps' to build models that can process and analyze large amounts of complex data to give accurate resultshese models are accurate and scalable and operate with a short turnaround time. By creating such accurate machine learning models businesses can taken advantage of profitable opportunities and avoid unknown risks.
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