can you vish?

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #2 (Regression) Machine Unlearning #3 (Clustering) Machine Unlearning #4 (Outlier Analysis) Reinforcement Learning is a slightly different learning model than the other techniques that we have discussed previously. Therefore, I wouldn’t be able to explain the same using the fruit basket example that we have been using all this while. Let’s replace apples and oranges with self-driving cars! Suppose that you are at Google or Tesla and are trying to train a car to drive by itself! How would you go about that? Driving requires the knowledge of much more than turning the ignition on and steering the wheel. You should know to keep the side of the road, to stop at the red signal, or to keep off the footpath, for instance. You decide

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #2 (Regression) Machine Unlearning #3 (Clustering) Machine Unlearning #5 (Reinforcement Learning) Let’s start with the example. We have the basket full of different fruits and we are sorting the fruits based on their distinctive properties, or features. What would happen if a coconut got mixed in the fruit basket that contained oranges, apples, bananas, cherries, and mangoes? The coconut does not share any feature (color, shape, or size) with any of those fruits in the basket. Since there is only in coconut in the basket, it is not possible to create a separate group of coconuts. Such items in the group, which stand out or are distinctive than the others are generally termed outliers. Outliers are sometimes discarded as noise

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #2 (Regression) Machine Unlearning #4 (Outlier Analysis) Machine Unlearning #5 (Reinforcement Learning) We have already gone through classification and prediction. Now let us see what clustering is. Another popular learning technique, clustering is different from the other two since it is an unsupervised learning technique. What does that mean? Let us revisit the classification technique. We show the machine an Orange and explains the features of the Orange to it. Similarly, each different fruit and its features are shown to the machine during the training phase. Once it has learned enough, we use the machine to label a randomly picked fruit. In clustering, such training does not take place. We present the system with a baske

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below: Machine Unlearning #0 (Intro) Machine Unlearning #1 (Classification) Machine Unlearning #3 (Clustering) Machine Unlearning #4 (Outlier Analysis) Machine Unlearning #5 (Reinforcement Learning) Regression is a popular machine learning technique used to predict target data based on a set of features. In classification, we train the system to assign distinctive labels to the object (Orange or Bananas in our previous example). Regression differs in the sense that here we are dealing with continuous variables. Suppose that you have twenty thousand rupees with you and are planning to buy a new phone. You open up an e-commerce site and search for phones. A hundred results appear. You see names like Redmi, Oppo, Vivo, Realme, Asus, Nokia, and so on, all in your target range of ten thousand to twenty thousand. You are cle

You can’t conclude a discussion on Machine Learning without mentioning classification. Classification is a machine learning technique where the machine is trained to predict the label of the given input data. Alright, let’s cut the jargon and get some real-world examples. Oranges and Bananas. Let’s assume that we have a box of fruits that contain some oranges and some bananas. You are asked to pick one fruit at random and tell if it is an orange or a banana. Pretty basic, right? For us, it is straightforward. We would know the answer at first sight. But, how would a computer be able to tell the difference? In classification, the machine would first be trained on some pre-labeled data. It would be shown an orange and we would tell it that the fruit is an orange. The machine would study the orange and remember its features - orange color and round shape. Then it would be shown a banana and the process is repeated. What are these features? A feature is anything that helps us uniquely labe

You might be familiar with the term Machine Learning. Worry not if you have not, cause I have tried to give a gist of the concept here. The term has been in the limelight of late and has been tossed around rather liberally to denote anything related to artificial intelligence, robotics, and data mining. Machine Learning, as the name suggests, could simply mean the field of study of enabling the “machines” (computers) to “learn” from past experiences and make informed decisions in the future.   Wait a minute! Learning from past experiences is something humans do, right? Exactly! The computer folks want computers to behave more and more like us. As if there aren't enough of us already. As the machines are becoming more like us, we are becoming more like them. Introspection time! Most of us wake up every morning like clockwork! Then we rush through the morning routines - get dressed, wade through the traffic, and reach our offices or schools or wherever people expect us to be. We spe