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ML Expert In One Simple Step



The web is full of good explanations of machine learning algorithms.

While it is important to understand the concepts behind the algorithms, one thing is even more important:

You need to learn how to apply machine learning algorithms.

Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data?

Choosing the right features, algorithms and parameters is an art. It's actually more like Karate than like math. You won't learn it from a book. You learn it by doing, by getting your hands dirty and applying algorithms to various data sets. By lots of trial and error. By having seen hundreds of successful applications.


To get better at applying machine learning techniques, 

Participate in Kaggle competitions.
Even the winning model of the Netflix prize has not been used in practice.
  1.  the access to various complex data sets combined with benchmarks on the leaderboard. 
  2. to study winning solutions. Don’t get scared by their complexity. You can ignore the stacking of hundreds of models and still learn important tricks from them. Whenever you are stuck on a particular data set you can visit the Kaggle Forum where others share loads of knowledge.
I usually join competitions where the features have a description. This is closer to reality. And in most business applications feature engineering is more important than your machine learning model.

 In a job you have to deliver value to the business. This involves many skills other than machine learning. For example, finding simple models that have enough predictive power, telling the signal from the noise and marketing your ideas to the rest of the company.
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