Supervised Learning (Part 4)

Welcome to the Part 4 of Supervised Machine Learning post. I hope you have gone through the previous posts on Machine Learning and Supervised Learning Part 1, Part 2 and Part 3.

If no, kindly go through those before starting from here. In this post we are going to see about the various applications of supervised machine learning and also the Advantages and Disadvantages of Supervised Learning.

In the previous posts we have discussed on the Supervised Learning, Types and Various Algorithms used in Supervised Machine Learning. Now here we can discuss about the applications of those algorithms with their Advantages and Disadvantages.

Applications of Supervised Learning

There are a lot of Applications of Supervised Learning used in the real world. Some of them are discussed below.

Speech Recognition is one of the significant applications of supervised machine learning. We use it daily on our smartphones. The voice assistant technology in our mobile and other gadgets uses this Supervised Learning. For example – SIRI, Google Assistant, Alexa. They use the speech recognition supervised learning algorithm to remember your voice and match it with them when you speak. They can assist you with anything in your smartphone. Also, these assistants come as separate devices too, you can connect your other electronics with Bluetooth if you want to activate them using the assistant. It also comes under security, especially for high-level security where you have to undergo several rounds of screening.

Object detection is a computer vision technique that allows us to identify and locate objects in a video or image. With this kind of identification and localization, object detection can be used to count objects in a frame and determine and track their precise locations and also labelling them accurately. Technologies such as raspberry pi are also working on this. It also uses computer vision.

Spam detection is the famous known application of Supervised Learning. We have seen the example of Spam detection in many previous posts. If there are any spam emails, it can help you to block such emails by classifying them as spam. It may even block them from sight. Its main purpose is to block fake things.

Prediction of stock markets – It can accurately predict the prices of the stock data by analyzing the pattern of previous data. We can make use of various algorithms for predicting the stock market. For example, it uses neural networks methods to predict the stock price. We are going to see about Deep Learning and Neural networks in the further posts in detail.

Advantages of Supervised Machine Learning

• Supervised Learning is very helpful in solving real-world computational problems.
• This type of learning is very easy to fathom. It is the most common type of learning method. For learning ML, people should start by practicing supervised learning as it gives a clear and detailed understanding about Machine Learning.
• Supervised learning allows you to collect data or produce a data output from the previous experience.
• The training data is only necessary for training the model. Since it is large it occupies a lot of space. But, its removed from the memory as it is of no importance after training is complete. So it saves a lot of computational space.
• We can improve its Accuracy further more by using some other models.

Disadvantages of Supervised Machine Learning

• Supervised Learning cannot create labels of its own. This means that, it cannot discover data on its own like unsupervised learning.
• Training for supervised learning needs a lot of computation time and power in case of Neural networks and Random Forest which all PC’s might not have.
•  If we enter new data, it has to be from any of the given classes only. If you enter Potato data in a collection of Tomato and Onion, it might classify the Potato into one of these classes, which won’t be right.
• Its performances are limited to the fact that it can’t handle complex problems in Machine Learning methods.

Summary

• In Supervised learning, you train the machine using data which is well labelled.
• Regression and Classification are two types of supervised machine learning techniques.
• Algorithms like Logistic Regression, Decision Tree are used for Classification problems and Algorithms like Linear Regression are used for Regression problems.
• Some Algorithms like KNN, SVM (Support Vector Machines) and Random Forest can be used for both Classification and Regression type problems.
• Supervised learning is a simpler method while Unsupervised learning is a complex method.
• The main advantage of supervised learning is that it allows you to collect data or produce a data output from the previous experience.

Thanks for reading. Do read the further posts and enhance your knowledge and skills in the Emerging Technologies. Please feel free to connect with me if you have any doubts. Do follow, support, like and subscribe this blog.

Fact of the day:

90% of the world’s data was generated within the past two years alone. Although the internet was invented half a century ago, around 90% of the world’s data was only produced in the last two years. The bulk of it comes from social media, digital photos, videos, customer data, and more. Just think of how much data will be generated in the next ten years😧.