Overview of "Practical Python Machine Learning Programming Notes"

Introduction

Introduction to the "Python Machine Learning Programming" series

This series covers articles related to the book "Python Machine Learning Programming: PyTorch & scikit-learn Edition" (First Edition, First Printing).
The great thing about this book is that it allows you to learn machine learning comprehensively by running Python code, reading algorithm explanations, and occasionally checking mathematical formulas.
It is recommended for those who want to learn machine learning with Python!
In this article, I will refer to this book as the "text".

Article Overview

[Chapters 1-2] Introduction: Python and Machine Learning Have Arrived

[Chapters 3-7] Development: A Lighthearted ML Life with sklearn

[Chapters 8-11] Twist: Before You Know It, You're in a Pitfall

[Chapters 12-17] Surprise: PyTorch, Descends

[Chapters 18-19] Conclusion: Into an Even More Complex World

Blog Introduction

I am writing eight series of articles on note.
Please feel free to take a look!

1. Relaxed Statistics

This is a blog that digs roughly into probability and statistics using the Statistical Test Grade 2 problem collection as a guide.
It's fine to treat it like casual conversation. Please feel free to take a look.
It corresponds to the Statistical Test Grade 2 Official Problem Collection CBT version.
There is also a distribution of sample code for Python and EXCEL.

2. Connecting with Statistics and Data Analysis

The series "Connecting with Statistics and Data Analysis" is a blog series that unearths "connections" to statistics and data analysis and presents them in a column style.
With the help of generative AI, I wrote these freely, imagining the gateway to statistics and data analysis.
For example...
- Connections between daily life and statistics
- Connections from Statistical Test Grade 2 to what lies beyond
I would be happy if you could read them at your leisure 🍀

3. Experiment! Fun Bayesian Modeling 1 & 2 with PyMC Ver. 5

I will draw and analyze the Bayesian models used in psychological research from the books "Fun Bayesian Modeling" and "Fun Bayesian Modeling 2" using PyMC Ver. 5.
Like this book, many Bayesian models are written in R language + Stan.
I will strive to explore the possibilities of PyMC and make Bayesian modeling easy to practice.
Since these are familiar and easy-to-visualize themes, please try running them with PyMC and let's enjoy it together!

4. Experiment! Iwanami Data Science 1 Bayesian Modeling with PyMC Ver. 5

I will draw and analyze Bayesian models by four Bayesians from the book "Experiment! Iwanami Data Science Vol. 1" using PyMC Ver. 5.
This book is an excellent resource for learning the basics of Bayesian programming.
I feel like I've become friends with Bayesian methods by having fun running PyMC models.
I hope you will all try running them with PyMC and join me in learning through play!

5. Fun Copying: Bayesian, Python, etc.

I am blogging about the results of my "book copying activities" for Bayesian, Python, and other topics.
I am mainly working on translating them into Python.
I hope this serves as sample code for fellow learners who are also copying code. 🍀

6. Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver. 5

I will practice the time series analysis from the book "Introduction to Time Series Analysis for Psychology with R and Stan" using Python and PyMC Ver. 5.
This book is packed with themes on time series analysis!
I truly realized the depth of time series analysis.
I will enjoy learning time series analysis with my favorite language, Python.

7. Writing about Data Science-like things

I write columns on statistics, data analysis, AI, machine learning, and Python on an irregular basis.
There are many articles related to statistics and data science books.
Series on "Statistics," "Python," "Mathematics and Python," and "R" have been created.

8. Python Machine Learning Programming Practice Notes

I wrote articles about my various thoughts while studying the book "Python Machine Learning Programming: PyTorch & scikit-learn Edition."
This book is a textbook for scikit-learn and PyTorch.
Please feel free to give it a try if you like.

Thank you very much for reading until the end.