Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1):

1. First you would look at what spam typically looks like. You might notice that

some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to

come up a lot in the subject. Perhaps you would also notice a few other patterns

in the sender’s name, the email’s body, and so on.

2. You would write a detection algorithm for each of the patterns that you noticed,

and your program would flag emails as spam if a number of these patterns are

detected.

3. You would test your program, and repeat steps 1 and 2 until it is good enough.

Since the problem is not trivial, your program will likely become a long list of com‐

plex rules—pretty hard to maintain.

In contrast, a spam filter based on Machine Learning techniques automatically learns

which words and phrases are good predictors of spam by detecting unusually fre‐

quent patterns of words in the spam examples compared to the ham examples

(Figure 1-2). The program is much shorter, easier to maintain, and most likely more

accurate 

Moreover, if spammers notice that all their emails containing “4U” are blocked, they

might start writing “For U” instead. A spam filter using traditional programming

techniques would need to be updated to flag “For U” emails. If spammers keep work‐

ing around your spam filter, you will need to keep writing new rules forever.

In contrast, a spam filter based on Machine Learning techniques automatically noti‐

ces that “For U” has become unusually frequent in spam flagged by users, and it starts

flagging them without your intervention (Figure 1-3).

Another area where Machine Learning shines is for problems that either are too com‐

plex for traditional approaches or have no known algorithm. For example, consider

speech recognition: say you want to start simple and write a program capable of dis‐

tinguishing the words “one” and “two.” You might notice that the word “two” starts

with a high-pitch sound (“T”), so you could hardcode an algorithm that measures

high-pitch sound intensity and use that to distinguish ones and twos. Obviously this

technique will not scale to thousands of words spoken by millions of very different

people in noisy environments and in dozens of languages. The best solution (at least

today) is to write an algorithm that learns by itself, given many example recordings

for each word.

Finally, Machine Learning can help humans learn (Figure 1-4): ML algorithms can be

inspected to see what they have learned (although for some algorithms this can be

tricky). For instance, once the spam filter has been trained on enough spam, it can

easily be inspected to reveal the list of words and combinations of words that it

believes are the best predictors of spam. Sometimes this will reveal unsuspected cor‐

relations or new trends, and thereby lead to a better understanding of the problem.

Applying ML techniques to dig into large amounts of data can help discover patterns

that were not immediately apparent. This is called data mining.

To summarize, Machine Learning is great for:

• Problems for which existing solutions require a lot of hand-tuning or long lists of

rules: one Machine Learning algorithm can often simplify code and perform bet‐

ter.

• Complex problems for which there is no good solution at all using a traditional

approach: the best Machine Learning techniques can find a solution.

• Fluctuating environments: a Machine Learning system can adapt to new data.

• Getting insights about complex problems and large amounts of data.