[Kaggle Study] 2024 How I am studying

My goal is to be a ranker in Kaggle community and I want to be somekind of running mate with readers of my blog. So I am planning to write some posts like this that records how I am studying. 

 

As of 2024.10.25, I have some background knowledge about data science because I took some undergraduate courses and studied for about 1 or 2 month before. So, I just started to compete in kaggle instead of taking any online lectures or else. 

 

I will separtate my posts into 2 categories: 1. Kaggle Study and 2. Kaggle Extra Study. 

 

[Kaggle Study] is posts about what I studied following curriculum documented by Yuhan Lee, Korean Kaggle Grandmaster. I will post summarization or further studies while following the curriculum. He picked kernels that Kaggle beginners should study for popular competitions in terms of each field of ml. 

• Updates:
  • 10/25: Decided to study some theory based books while following the curriculum. I am planning to study only parts that I want to know further and summarize into a post. book1 book2

[Kaggle Extra Study] is posts about what I studied while competing in Kaggle competitions. 

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I will summarize the posts below:

[Kaggle Study]

• Loss function + Gradient Descent
• Backpropagation
• More about relationship between loss function and gradient descent
•  

[Kaggle Extra Study]

• Supervised learning vs. Unsupervised Learning
• AutoEncoder
• Time-series data
• Curse of dimensionality
• Cross validation
• Ensembling
•  

 

 

“Courage is the commitment to begin without any guarantee of success”
― 
Johann Wolfgang von Goethe