Step-by-step to a Data Scientist

Step-by-step to a Data Scientist > Blog > for beginner > List: PyCaret Articles

PyCaret is a useful auto ML python library because we can deploy machine learning models with low codes. We can also perform preprocessing, compare models, and tune hyperparameters, of course with low codes.

This article is a summary of the list of the PyCaret articles introduced in this blog.

Dockerfile for PyCaret

We create a docker image for PyCaret from Dockerfile. This post is intended for mastering how to build a docker image from Dockerfile with docker commands.

Tutorial of PyCaret, Regression Analysis

This post is for beginners.

In this post, we will see the tutorial of PyCaret with a regression analysis against the Boston house prices dataset. This post is intended with the step-by-step guide in mind.

Prediction of Diabetes Progression by PyCaret, Regression Analysis

This post is one of the good examples of regression analysis.

The purpose is to learn the basics of regression analysis using PyCaret. Using a famous data set, we will master the basics of everything from model construction to analysis of results.

PyCaret 2.3.6, incredible update; Convert model and Web App
PyCaret 2.3.6, incredible update; Dashboard and EDA functions

PyCaret was fully updated in version 2.3.6.

In version 2.3.6, several new features were added. In this article, You can check the major changes. These articles are also worth reading to get an idea of the latest new features in PyCaret.

Step-by-step to a Data Scientist > Blog > Weekly Article News > Weekly Article News #29

The recommended articles the author has read this week.
This letter is posted every Monday.

This week, the articles are about a Machine learning project environment, e. g. MLflow, and Jupyter Notebook.

In the environment of a machine learning project, it is very important to prepare not only a data analysis environment such as Jupyter Notebook but also an MLflow environment for managing experiment records. Managing experiment records helps to improve reproducibility and project promotion efficiency.

Containerize your whole Data Science Environment (or anything you want) with Docker-Compose

To build the environment of a machine learning project, docker-compose is a powerful tool. By docker-compose, we can build data-analysis and experiment-management experiments separately. This article tells us how to build such a style environment.

Manage your machine learning life cycle with MLflow in Python

This article shows one example of how to use the MLflow tracking server, which is a tool for managing experiment records. There are several styles to build the MLflow tracking server. This article suggests one of the helpful styles.