Hi, and Welcome to my GitHub Blog.
\( *^ω^*)/ ヤッター!
All of my posts here are the memo from my self-study courses, articles, and books. I hope that some of these will help you along in your own journey of learning data science.
If you find the blog posts useful, please give the GitHub blog repo a star to show your support and share it to others. Also, please let me know in the comments section of the posts if you have any questions.
If you would like to subscribe, please click the Subscribe button at the bottom left of the page. Please note that you may have to create or log in to your Feedrabbit (an RSS feed free service), as I use Feedrabbit to send email notifications about new posts.
Thank you! (´・ω・`)ゞ アリガトウ ヨロシク♪.
Posts
You'll learn the basics of manipulating time series data.
Aug 25, 2022
NumPy is an essential Python library. TensorFlow and scikit-learn use NumPy arrays as inputs, and pandas and Matplotlib are built on top of NumPy. In this Introduction to NumPy course, you'll become a master wrangler of NumPy's core object - arrays!
Aug 23, 2022
Chapter 4 - Preprocessing and Pipelines
Aug 20, 2022
Chapter 3 - How good is your model?
Aug 19, 2022
Chapter 2 - Regression
Aug 18, 2022
Chapter 1 - Classification
Aug 17, 2022
Modeling - Time to bring everything together and build some models! In this last chapter, you will build a base model before tuning some hyperparameters and improving your results with ensembles. You will then get some final tips and tricks to help you compete more efficiently.
Aug 6, 2022
Feature Engineering - You will now get exposure to different types of features. You will modify existing features and create new ones. Also, you will treat the missing data accordingly.
Aug 5, 2022
Now that you know the basics of Kaggle competitions, you will learn how to study the specific problem at hand. You will practice EDA and get to establish correct local validation strategies. You will also learn about data leakage.
Aug 4, 2022