Category: machine learning

intermediate machine learning course. I don’t remember when I started on that course. I assume over a year ago.

I think I have down when I need to do for missing values, categorical values. I know a little bit how to use a pipeline, cross validation and XGboost.

I know I need to practice what I learned from the course or I will forget it. I don’t think I will ever be ready to do my own machine learning projects. But, I think getting a machine learning project is what I need to do.

Also the intermediate learning course talked about data leakage. It talked about two kinds of leakage target leakage, and train test spit leakage. At first I did not understand what leakage meant. I think now I have a better idea.

I still have about ten more kaggle tutorials to go through to have completed them all. Probably about 14 to be exact.

I think next I will go through some of the courses I have not finished and work through the feature engineering tutorial and also try a machine learning project. Maybe I will try a competition.

able to remember things better. I read the lesson about how to deal with missing values again. I can either delete the missing values, imputer the values or use an extension to values.

And imputing means that I add values for the missing values. And the extension to imputing is adding values and adding a column that says which values were imputed. It was so hard for me to remember those things when I first read about imputation.

trouble remembering what I had trouble remember the steps I needed to go through to what is it train the model.

I remembered that I needed to load the csv file into a variable. Then after that I could not remember what to do. Now I think I have got the steps down.

Then what I need to do is load the prediction target into a variable. Then I make a list of features. Then I load the features into a variable. Then I take the variable with the features and put it into a data frame.

Then I select the machine learning model I want to use from sci-kit learn, next up is fitting the model, then it time to predict.

Oh, and I forgot that I need to drop missing features from a dataset before I make the variable for the prediction target.

Maybe writing the steps out will help me remember them better.

arxiv.org for awhile and yesterday I thought why not get some machine learning papers from arxiv and read them. I have gotten math papers from arxiv but, I never thought about getting machine learning or security papers from there. Seems interesting.

kaggle’s machine learning course. Its still hard for me to remember what I am supposed to be doing to get a prediction. I will have to keep trying. I have got to walk before I can run. Just because I have trouble remember what to do know does not mean I will have trouble remembering how to get a prediction a year from now.

It was exciting to get to submit a prediction to a kaggle contest. At the end of the machine learning course I got to submit my results to a kaggle contest. I had no idea that the machine learning model I had been working on in the course would be submitted to a kaggle contest.

I got an error when I tried to submit my notebook to the contest but, I checked and it had actually submitted to the contest. So after 3 years of being on kaggle I have finally submitted something to a contest. Very cool.

Then I started going through the intermediate machine learning tutorial. One course down the rest of them to go. Since I hope to go through all of the kaggle courses.