Mix JAVA and SCALA in a Single IntelliJ Project Module

image created by author


Hands-on: Set up a Docker development environment on Windows 10

Image by Richard Sagredo. https://unsplash.com/photos/ZC2PWF4jTHc


This is a tutorial to walk through the NLP model preparation pipeline: tokenization, sequence padding, word embeddings, and Embedding layer setups.

NLP model preparation steps, (created by the author)

Intro: why I wrote this post


This article explains what a confusion matrix is and how to use it.

Photo by Emily Morter On Unsplash
  • Part I: confusion matrix.
  • Part II: accuracy, recall, precision, f1-score.
  • Part III: soft-metrics: ROC…


lighter | faster | safer | flexible | fool-proof

Tutorials on using Pandas ‘category’ data type in Python

resource: Kung Fu Panda: The Paws of Destiny


PLOTLY | GEOGRAPHIC | DATA VISUALIZATION | DATA SCIENCE | PYTHON

A step by step guide on exploratory data analysis and interactive dashboard presentation

Example of geographic data visualization using Plotly (in this post)

Why Plotly?

  • Visually appealing. You have to try hard to make it look ugly 😜.


Try to use PCA but stuck at processing stage? Check this out!

Siyun Wang (co-author of this article). A fluffy bird on a branch [water color] (2020). We used low-poly art to indicate the feeling of PCA, which is dimensionality reduction. But PCA can do more than that.

Intro of the series

  • Part I: Scalers and PCA
  • Part II: Meet outliers
  • Part III: Categorical data encoding

What we will do in this post

  1. Introduce/review the dataset to work on and the task
  2. Add synthetic outliers to the original dataset
  3. Perform scaling-transformation on the modified dataset
  4. Conduct PCA on the scaling-transformed dataset and evaluate the performance

What you will learn

  • Understand the importance of scalers and their close relationship with PCA


Try to use PCA but stuck at the processing stage? Check this out!

Siyun Wang. A fluffy bird on a branch [water color] (2020). We used low-poly art to indicate the feeling of PCA, which is dimensionality reduction. But PCA can do more than that.

Intro of the series

  • Part I: Scalers and PCA
  • Part II: Meet outliers
  • Part III: Categorical data encoding

What we will do in this post

  1. Review briefly the background of scalers and PCA
  2. Introduce the dataset to work on and the task
  3. Perform scaling-transformation on the dataset
  4. Conduct PCA on the scaling-transformed dataset and evaluate the performance

What you will learn

  • Understand the importance of scalers and their close relationship with PCA

Kefei Mo

ML engineer / data story teller / electrical engineer / digital illustrator / 3D modeler. I am writing data science blog with my cousin.

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