Contents tagged with Machine learning

  • How to measure quality of learning

    Hello everybody,

    Today I want to describe some ideas about measure quality of learning. 

    First of all I want to point areas where you can apply those measurements. It can be in three areas:

    For setting funtional during learning

    For picking hyperparameters

    For evaluation of ready made model

    Another way can be combination. You can measure quality during learning with one measurement, but final model you can analyze with other measurement. 


    So, let's start with most common formula: mean squared error:

    In words it reads the following: difference between prognozed value and desired value, squared, summed and finally averaged. 

    MSE has following featues:

    Easily minimizable … more

  • Types of learning in AI

    Hello everybody,

    today I want to make a short notice on question that I often receive: what kinds of learning exists in Machine Learning. I want to provide simple answer:

    Learning with teacher: questions and answers.

    Learning with teacher: just questions

    Partial learning: questions, and some of them with answers

    Active learning: at which object you'll get an answer

    I suppose maybe some variations of this also exists, but as usually ML in one or another way manipulates with those four. 

    Consider example of clusterization. You have some data set of values. And you need somehow to group them or in other words find groups of similar objects. This task has two problems: nobody know … more

  • Machine learning certificate from coursera

    Hello everybody,

    I want to boast that I receved certificate from Standfor Univercity about my level of knowledge in Machine Learning.

    Here is the link:

    and here is screenshot:


  • Principal Component Analysis in Machine Learning

    Hello everybody,

    today I want to note important for me details of Machine Learning. 

    So, the first and very important usage of PCA is visualizing data. If you have 10 dimensions, can you visualize those data? If you can I'm happy about you, but I can't. I can imagine only 1, 2, 3 D :). But with principal componenet analysis it's possible to visualize data. 

    Second application is reducing memory/disk need to store data. That's quite self-explanatory, to train on 10 000 dimensions and 100 dimensions is different.

    Third is speeding up learning algorithm. It's actually related with second.

    Another important detail, it's bad idea to use PCA in order to avoid overfitting. Actually everybody … more

  • Debugging learning algorithm

    Few notes of how to debug machine learning algorithm:

    Get more training examples

    Try smaller sets of features

    Try getting additional features

    Try adding polinomial features

    Try increasing lambda

    Try decreasing lambda

    What those trys can achieve:

    fixes high variance

    fixes high variance

    fixes high bias

    fixes high bias

    fixes high bias

    fixes high variance