Supervised & Unsupervised Machine Learning Techniques

We all Know that Computers & Mobile devices getting smarter and smarter day by day, but these all are hardware. They are doing so with the help of NPU(Neural Processing Unit)  & Quantum Computing(which is in premature state right now). Apart from these two all the magic of AI & ML comes from software side like predicting Search Results whenever you start typing, predicting physical objects, predicting Human mood by analyzing facial pattern. These all are very fascinating and out of the world things which was only imagination 20-30 years before & now we use them on daily basis. So here is two popular & widely used algorithm.

Supervised Learning:-

Supervised learning is an approach to Machine Learning that is based on training data that includes expected answers. An Artificial Intelligence uses the data to build general models that map the data to the correct answer.

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.

                                                                         Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that 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.

In a simple Language, Supervised Learning is a presence of supervisor as teacher. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with correct answer. After that, machine is provided with new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces an correct outcome from labeled data.

Supervised learning problems can be further grouped into regression and classification problems.

•  Classification- A classification problem is when the output variable is a category, such as “red” and “blue” or “disease” and “no disease”.
• Regression- A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
  

Real World example of Supervised Learning are:--

1. Visual Recognition.
2. Sorting Robotic Path.
3. Weather Prediction.

Unsupervised Learning:-

Unsupervised learning is an approach to machine learning whereby software learns from data without being given correct answers. It is an important type of artificial intelligence as it allows an AI to self-improve based on large, diverse data sets such as real world experience.

Unsupervised learning is where you only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

A central application of unsupervised learning is in the field of density estimation in statistics, though unsupervised learning encompasses many other problems (and solutions) involving summarizing and explaining various key features of data.

In a General Language, Unsupervised Learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabeled data by our-self.

Unsupervised learning classified into two categories of algorithms:

• Clustering- A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
• Association- An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.