First encounter with machine learning: Machine learning starting from theory and building automated analysis tools through LLM (feat. (A true battle between students and models)

📚 Course introduction

“This is the beginning of a revolutionary journey where even a beginner in coding can become an AI expert.”

From Python's basic grammar to object-oriented programming, Core principles of machine learningand using the latest LLM technology Automatic data analysis tools Up to development - you can master all this in one lesson.

We'll also share all the completed codes 😄

✨ What will I learn?

🐍 Complete conquest of Python

Basics of variables, data types, conditional statements, and iterations
Efficient coding using functions and modules
We even provide practice codes for practical exercises:)

📊 Essential tools for data analysis

Practical usage of Pandas, NumPy, and Matplotlib
Data visualization and pre-processing techniques
We even provide practice codes for practical exercises:)

🤖 Machine learning master

Understanding supervised learning/unsupervised learning algorithms
Implementation of actual projects through data
Let's compete by comparing the performance of the model I created with the performance of the students' models on the dashboard!

🚀 Utilizing the latest technology

Create an AI automated analysis tool that can be completed in just a few typing steps with Streamlit+LLM!
Integration and use of GPT series model APIs
If your company has a generative AI page What if it gets stuck?
[Offer code 1] QnA chatbot implementation code that reads the PDF and answers the user's questions when the PDF is uploaded
[Offer code 2] When a CSV is uploaded, the content is read and automatically visualized + machine learning code + QnA chatbot function implementation code for additional code tasks

🎯 I recommend it to people like this

Beginner with no coding experience
Anyone interested in AI and machine learning
Those who want to start their own AI project
Automated data analysis toolsThose who want to make

💻 How to learn

It is a course that combines theory and practice in a balanced manner, and each class includes practical coding exercises. At the end of the course Your own AI automation toolYou will be able to complete it! This is because you can simply upload the data you want to analyze yourself Talk to GenAI on a computer and analyzeIt will allow you to do it:)

“The AI revolution has already begun. Now it's your turn to be the protagonist.”

1. Python basics

1.1 Getting started with Python

What is Python?
Installing a Jupyter notebook
Learn simple controls
Variables, data types, and operators

1.2 Python Programming Fundamentals 1

Conditional statement
loop
function
Lists, dictionaries

1.3 Python Programming Basics 2

Numpy
Matplotlib
Pandas

2. Fundamentals of data analysis

2.1 Machine Learning Overview

What is machine learning (machine learning)?
Supervised learning
How to evaluate learning models
Overfit, underfit

2.2 Machine learning (regression)

Learn about different regression models
Hyper parameter tuning
Learn regression models
Predicting regression models
Evaluating regression models

2.3 Machine learning (classification)

Explore the different classification models
Classification model performance evaluation metrics
Practice learning, predicting, and evaluating classification models

2.4 (Appendix) Competing for machine learning performance!

Go to https://whoisthewinner.streamlit.app/
Measure the performance of the models I've learned and compete with the students' models! (Let's compete just for fun!)

3. Development of automated data analysis tools using LLM and Streamlit

5.1 LLM Basics and Uses

Understanding LLM concepts
How to use the OpenAI API

5.2 Integrating LLM with Streamlit

Automating data analysis with LLM
User interface improvements
assignments: Complete a file upload and LLM-based data analysis chatbot web app

Final project

1) Development of automated data analysis tools (integration of all lessons)

Data upload function
LLM-based data analytics chatbot
Visualization dashboards
Ability to save and share results
Final task: Development and presentation of a complete data analysis automation tool using actual datasets

First encounter with machine learning & AI: <automated data analysis tools created by data scientists at large companies >

First encounter with machine learning & AI: <automated data analysis tools created by data scientists at large companies >