How does explain machine learning and Data Mining to non computer science people?Pararth Shah, ML enthusiast answered Dec, Shenzhen Featured on VentureBeat • Upvoted by Melissa Dalis, CS & Math Major at Duke and Alberto Bietti, PhD student in Machine learn Ing. Former ML engineer
Mango Shopping
Suppose you go shopping for mangoes one day. The vendor have laid out a cart full of mangoes. You can handpick the mangoes, the vendor would weigh them, and you pay according to a fixed Rs per Kg rate (typical story I n India).
Obviously, want to pick the sweetest, most ripe mangoes for yourself (since is paying by weight and not by qualit Y). How does you choose the mangoes?
You remember your grandmother saying that bright yellow mangoes is sweeter than pale yellow ones. A simple Rule:pick is only from the bright yellow mangoes. Check the color of the mangoes, pick the bright yellow ones, pay up, and return home. Happy ending?
Not quite.
Life is complicated
Suppose you go home and taste the mangoes. Some of them is not sweet as you ' d like. You are worried. Apparently, your grandmother ' s wisdom is insufficient. There is more to mangoes than just color.
After a IoT of pondering (and tasting different types of mangoes), you conclude that the bigger, bright yellow mangoes is Guaranteed to is sweet, while the smaller, bright yellow mangoes is sweet only half the time (i.e. if you buy bright Yellow mangoes, out of which is big in size and is small, then the big mangoes'll all is sweet, while out of The small ones, on average only mangoes would turn out to be sweet).
You is happy with your findings, and your keep them in mind the next time you go mango shopping. But next time in the market, you see that your favorite vendor have gone out of town. You decide to buy from a different vendor, who supplies mangoes grown from a different part of the country. Now, you realize this rule which you had learnt (so big, bright yellow mangoes is the sweetest) is no longer applic Able. Learn from scratch. You taste a mango of all kind from this vendor, and realize that the small, pale yellow ones is in fact the sweetest of All.
Now, a distant cousin visits to another city. You decide to treat she with mangoes. But she mentions this she doesn ' t care about the sweetness of a mango, she's only wants the most juicy ones. Once again, you run your experiments, tasting all kinds of mangoes, and realizing that the softer ones is more juicy.
Now, you move to a different part of the world. Here, mangoes taste surprisingly different from your home country. You realize the green mangoes is in fact tastier than the yellow ones.
You are marry someone who hates mangoes. She loves apples instead. You go apple shopping. Now, all your accumulated knowledge on mangoes is worthless. Learn everything about the correlation between the physical characteristics and the taste of apples, by the SA Me method of experimentation. You do it, because.
Enter Computer Programs
Now, imagine the all this while, you were writing a computer program to help you choose your mangoes (or apples). You would write rules of the following kind:
if (color is bright yellow
andSize is big
andSold by favorite vendor): Mango is sweet.
if (soft): Mango is juicy.
etc.
You would use these rules to choose the mangoes. You could even send your younger brother with this list of rules to buy the mangoes, and you would is assured that he'll Pick only the mangoes of your choice.
But every time you make a new observation from your experiments and you have to manually modify the list of rules. Understand the intricate details of all the factors affecting the quality of mangoes. If the problem gets complicated enough, it can get really difficult to make accurate rules by hand this cover all possible Types of mangoes. Your could earn you a PhD in Mango science (if there is one).
But does everyone have that kind of time.
Enter machine learning Algorithms
ML algorithms is an evolution over normal algorithms. They make your programs "smarter", by allowing them to automatically learn from the data you provide.
A randomly selected specimen of mangoes from the market (
Training Data), make a table of all the physical characteristics of each mango, like color, size, shape, grown in which part of the Cou Ntry, sold by which vendor, etc (
features), along with the sweetness, juicyness, ripeness of that Mango (
Output Variables). You feed this data to the Machine learning algorithm (
classification/regression), and it learns a model of the correlation between an average mango ' s physical characteristics, and its quality.
Next time you go to the market, you measure the characteristics of the mangoes on sale (
test Data), and feed it to the ML algorithm. It would use the model computed earlier to predict which mangoes is sweet, ripe and/or juicy. The algorithm may internally use rules similar to the rules you manually wrote earlier (for Eg, a
Decision Tree), or it may use the something more involved, but you don ' t need to worry on that, to a large extent.
Voila, can now shop for mangoes with great confidence, without worrying about the details of what to choose the best ma Ngoes. And what's more, you can make your algorithm improve over time (
Reinforcement Learning), so that it would improve its accuracy as it reads more training data, and modifies itself when it makes a wrong predicti On. But the best part was, you can use the same algorithm to train different models, one all for predicting the quality of app Les, oranges, bananas, grapes, cherries and watermelons, and keep all your loved ones happy:)
And that, are machine learning for you. Tell me if it isn ' t cool.
Machine Learning: Making your algorithms smart, so then you don ' t need to be. ;)677.6k views • View upvoters · not for ReproductionThis is clearly reproduced in Kazakhstan
How does explain machine learning and Data Mining to non computer science people?
