Ch2: People who bought this also bought…… ( what is ML? )

Meenu was working from home that day, alone and immersed in her tasks, when a knock interrupted her concentration. It was a delivery guy with a package. She thanked him, took the package inside, and returned to her work. However, just thirty minutes later, another knock echoed through her quiet home. This pattern continued throughout the day, as four deliveries intended for her mother arrived in succession. Each knock seemed perfectly timed to disrupt her just as she settled into a meeting, leaving Meenu increasingly frustrated.

By evening, when her mother finally returned, Meenu was waiting, surrounded by packages. "Amma! You're back," she exclaimed, relief and annoyance mingling in her voice. "There were so many deliveries today! What did you order?"

"Oh, Meenu," her mother began with a sheepish grin as she started unpacking the items. "Initially, I only planned to order a bedside table. But then, the app suggested a lamp, and it was so pretty! Next, it showed some scented candles, and then a throw. Each suggestion seemed to complement the bedside table so well, I ended up buying them all."

Meenu shook her head, amused yet exasperated. "Amma, you've been tricked into buying all these things using machine learning."

"Tricked? But I like everything I bought. And what does machine learning have to do with this?" her mother asked, genuinely puzzled.

"Yes, Amma, the products are good. They didn't trick you by giving you poor quality, but they used machine learning and data analytics to make you buy things you didn't actually need. Initially, you just wanted a bedside table, but the app manipulated you into buying ten more items!" Meenu explained.

Her mother looked bewildered. "But how did the app know that a table needs a lamp and all the other items?"

"That's the work of ML, Amma," Meenu said. "Remember, I mentioned I would explain machine learning to you? Now's the perfect time. Once you understand, you'll see what's happening here."

Meenu settled next to her mother, ready to demystify the concept. "Machine Learning allows computers to learn and make decisions on their own without being explicitly programmed for each task. It's like teaching a computer to figure things out by itself, by showing it many examples and letting it recognize patterns."

"Let me break it down further into its subsets," Meenu continued:

Supervised Learning: "This involves training a model on a labeled dataset, where the outcomes are already known. Imagine you're learning to recognize different types of animals, and you have a guide who shows you pictures and tells you what each animal is. You use these examples to learn and later, when shown a new picture, you can identify the animal based on your previous learning. Similarly, if we have data on houses in Hyderabad—including their size, location, and price—we can predict a house's price based on these factors because the model has been trained with examples where the outcomes are known."

Unsupervised Learning: "In contrast to supervised learning, unsupervised learning works with unlabeled data. This method looks for patterns and relationships without any pre-existing labels. For instance, if we analyze shopping data from a supermarket, we might discover that people who buy bread also tend to buy jam. The model groups these items together based on customer buying patterns, not because someone told it to. That's how the shopping app suggested the lamp and candles to you; it recognized a pattern from other users' purchases."

Reinforcement Learning: "This method involves learning through trial and error, using feedback from actions to learn behaviors. It’s akin to training a pet with treats for good behavior. The machine makes decisions, receives feedback, and adjusts its actions accordingly. Over time, it learns to optimize its behavior to maximize rewards."

"Now, Amma, do you see how you were subtly influenced?"

Her mother nodded, a look of realization dawning on her face. "Yes, I see now. I need to be cautious with those 'suggested for you' and 'people who bought this also bought...' messages."

"Exactly!" Meenu smiled, pleased with her explanation. "Being informed helps you make better choices."