Artificial Intelligence (AI) and Machine Learning (ML) are two of most discussed topics today in industry, and rightfully so. It is much like how internet emerged as a game changer in everyone’s life, Artificial Intelligence and Machine Learning are poised to transform our lives which were unimaginable few years ago. Often, we tend to use the terms Artificial Intelligence (AI) and Machine Learning (ML) synonymously. However, these two terms are very different – machine learning is one among the crucial aspects of the much broader field of AI.
They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. I have already written about Artificial Intelligence. So i thought let’s write down, What is Machine Learning ?
What is Machine Learning ?
So what exactly is “machine learning” anyway? ML is actually a lot of things. Machine Learning is about making predictions. Machine learning is a school of computer science that focuses on programming machines to improve their own performance through data and iteration.
Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. and later improve from experience without being explicitly programmed. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.The field is quite vast and is expanding rapidly, being continually partitioned and sub-partitioned into different sub-specialties and types of Machine Learning.
According to SAS,
“Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”
Machine Learning Methods :
Machine Learning Algorithm Basically can be Divided into three :
- Supervised Learning(Task Driven)
- Unsupervised Learning(Data Driven)
- Reinforcement Learning(Learning From Environment)
Supervised Learning :
Supervised learning is a common task in machine learning that works by using input and output pairs to train an algorithm.The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.
It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.
Unsupervised Learning :
In unsupervised learning the system iterates without the labeled, structured data that teams use to train supervised algorithms. Another way of putting this is that the algorithm has to train only on inputs without knowing the corresponding outputs.The program is given a bunch of data and must find patterns and relationships therein.
Unsupervised learning is ideal for transactional data applications, such as identifying customer segments and clusters with specific attributes.Unsupervised learning algorithms are mostly used in creating personalized content for individual user groups. Online recommendations on shopping platforms and identification of data outliers are two great examples of unsupervised learning.
Reinforcement Learning :
Reinforcement learning is quite similar to traditional data analysis method where the algorithms learn through trial and error method, after which it declares the outcomes with the best possible results. Reinforcement Learning is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Steps Involved In Machine Learning :
There are 5 basic steps used to perform a machine learning task:
- Data Gathering
- Data Cleaning and Preparation
- Training a model: This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts — train and test (proportion depending on the prerequisites); the first part (training data) is used for developing the model. The second part (test data), is used as a reference.
- Evaluating the model
- Improving the performance.
Why is Machine Learning Important ?
By now we know what is machine learning but now comes the question – Why is machine learning so important to business ?
Machine Learning has already drastically altered the business landscape.Primary focus of machine learning is to help organizations enhance their overall functioning, productivity, and decision-making process by delving into the vast amounts of data reserves. As machines begin to learn through algorithms, it will help businesses to unravel such patterns within the data that can help them make better decisions without the need for human intervention.
For example , moment you open the home page of ecommerce site, you receive highly accurate recommendations. This can benefit both the seller and the buyer. Several other examples are there like car computer system , weather system , cyber security, financial analysis , healthcare etc which are using ML and getting benefits.
Final Notes :
We’ve covered the basic theory underlying the field of Machine Learning here, but of course, we have only barely scratched the surface. Clearly, Machine Learning is an incredibly powerful tool. In the coming years, it promises to help solve some of our most pressing problems, as well as open up whole new worlds of opportunity. Hope you like this article !!