Question

Describe two supervised, semi-supervised and unsupervised learning methods and also give practical scenarios where these methods are most appropriate.

Answer 1

(a)Supervised:-

• The data that you have collected here is labelled and so you know what input needs to be mapped to what output.
• Learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer.
• Supervised learning is a simpler method.
• Highly accurate and trustworthy method.
• Number of classes is known.
• Classifying big data can be a real challenge in Supervised Learning.
• Supervised learning model uses training data to learn a link between the input and the outputs.
• Algorithm used Support vector machine, Neural network, Linear and logistics regression, random forest, and Classification trees.

Example:-

1. you want to train a machine to help you predict how long it will take you to drive home from your workplace. Here, you start by creating a set of labeled data. This data includes

• Weather conditions
• Time of the day
• Holidays

All these details are your inputs. The output is the amount of time it took to drive back home on that specific day.

2.This is similar to a teacher-student scenario. There is a teacher who guides the student to learn from books and   other materials. The student is then tested and if correct, the student passes. Else, the teacher tunes the student and makes the student learn from the mistakes that he or she had made in the past.

3.Suppose you have a daughter who has just turned 3 years old and is learning to speak. She knows the words, Dad and Mom, as her parents have taught her how she needs to call them. You want to teach her what a dog and a cat is. So what do you do? You either show her videos of dogs and cats or you bring a dog and a cat and show them to her in real-life so that she can understand how they are different.

Now there are certain things you tell her so that she understands the differences between the 2 animals.

• Dogs have a long mouth while cats have smaller mouths.
• Dogs bark while cats meow.
• Different dogs have different ears while cats have almost the same kind of ears

• Dogs and cats both have 4 legs and a tail.
• Dogs come in small to large sizes. Cats, on the other hand, are always small..

4.Suppose we are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:

If shape of object is long curving cylinder having color Green-Yellow then it will be labeled as –Banana.

(b)Semi-supervised:-

• combines a small amount of labeled data with a large amount of unlabeled data during training.
• Semi-supervised machine learning is a combination of supervised and unsupervised machine learning methods.
• Semi-supervised learning uses both tagged and untagged data to fit a model.
• The goal of a semi-supervised model is to classify some of the unlabeled data using the labeled information set.

Example:-Web Content Classification,Speech Analysis etc.

(c)Unsupervised:-

• The training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
• Unsupervised learning is computationally complex.
• Unsupervised learning does not use output data.
• You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known.
• The task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.
• Learning method takes place in real time.
• Unsupervised algorithms can be divided into different categories: like Cluster algorithms, K-means, Hierarchical clustering, etc.
• Algorithms are used against data which is not labeled.
• Unsupervised learning is a machine learning technique, where you do not need to supervise the model.
• The data collected here has no labels and you are unsure about the outputs. So you model your algorithm such that it can understand patterns from the data and output the required answer. You do not interfere when the algorithm learns.
• 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.

Example:-

1. suppose an image having both dogs and cats which have not seen ever.

   Thus the machine has no idea about the features of dogs and cat so we can’t categorize it in dogs and cats. But it can categorize them according to their similarities, patterns, and differences i.e., we can easily categorize the picture into two parts. First first may contain all pics having dogs in it and second part may contain all pics having cats in it. Here you didn’t learn anything before, means no training data or examples.

   It allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with unlabelled data.

   2.Let's, take the case of a baby and her family dog.He knows and identifies this dog. A few weeks later a family friend brings along a dog and tries to play with the baby.Baby has not seen this dog earlier. But it recognizes many features (2 ears, eyes, walking on 4 legs) are like her pet dog. He identifies a new animal like a dog. This is unsupervised learning, where you are not taught but you learn from the data (in this case data about a dog.) Had this been supervised learning, the family friend would have told the baby that it's a dog.