Data preprocessing and processing using the Pandas library
Data visualization with Matplotlib and Seaborn libraries
Machine learning theory and practice using the scikit-learn library
Practical training using Kaggle data
With “rough but useful” rough coding, Building Python Machine Learning from the Ground Up 📖
Want to takeyour first steps in machine learning ?
What you must know to start machine learning Basic libraries and Learn about real-world machine learning models! #Pandas #Matplotlib #Seaborn
Oh, are you talking about me?
Machine learning is popular these days I know it's good, but I'm so lost as to where to start .
I have already learned machine learning It is being applied, but I'm not sure if I know this correctly .
Machine Learning: Why is it important?
Machine learning is becoming increasingly important! Machine learning is the process of programming computers to learn from data using various statistical algorithms. By the way, do you know why we use machine learning?
For example, let's take the case of creating a filter to handle spam within a service using traditional techniques. In this case, we would create a spam filter like this:
Detects sentence patterns containing words and phrases that are commonly found in spam, such as 'credit card', 'free', 'advertisement', and 'loan'.
Create an algorithm that detects sentence patterns to classify email spam.
Algorithm testing and evaluation are conducted.
The above approach may seem simple, but as the problem becomes more complex and the number of rules increases, maintenance becomes difficult. On the other hand, machine learning can significantly improve maintainability and accuracy by automatically learning patterns that occur in spam.
So we You need to learn machine learning!
Machine learning learning What if it just felt difficult?
As machine learning technology becomes widely known and receives much love and popularity, there are countless related courses available. However, most of these courses follow a similar pattern, focusing only on the topic or concept in a rigid manner. They lack explanations of how it can be applied and utilized in practice.
So, unlike other lectures, this lecture doesn't go straight into the topic of machine learning. Instead, we will learn about the libraries that are absolutely necessary before actually doing machine learning, while freely preprocessing and visualizing real data , and then learn about the overall machine learning concepts.
So that you can learn machine learning 'properly'.
💡 You can set the direction to start machine learning.
💡 You can learn the basic concepts of machine learning.
💡 You can develop the capabilities needed for analysis in addition to machine learning.
Based on the know-how we've accumulated over the years, we'll help you learn machine learning effectively. Would you like to try machine learning together?
Attention, these people!
In Python data analysis Anyone interested
Studying machine learning For beginners
Data preprocessing and processing Those who want to learn
Machine learning theory Those who want to review
Please check your player knowledge!
You should know the basic grammar of the programming language Python .
What do you learn?
Kaggle
Pandas
Matplotlib
Scikit-Learn
In this lecture Check out the benefits.
Just the essentials!
Many other products on the market Unlike machine learning lectures, Only the essential content Let me give you a brief introduction.
Level up through practice
It doesn't stop at theory scikit-learn built-in and Using Kaggle data We provide practical training.
Machine Learning for Beginners
Knowing the basics of Python Tailored to beginners' level Not difficult You can learn the concept.
Data analysis too?
Not only machine learning concepts Required for data analysis Using the library We will also introduce it.
So that you can learn machine learning 'properly'.
✅ I will teach you effective study methods based on the know-how I have acquired through learning machine learning so far.
✅ We'll help you recall confusing concepts through theoretical lectures on overall machine learning models.
✅ If you have any questions while studying, please feel free to leave them. I'll try to answer them.
Machine learning built from the ground up, Let's learn in order!
Week 1: Colab setup and basic hands-on experience with the Pandas library.
Data preprocessing using the Pandas library
Loading and Saving Data
Series
DataFrame
Selecting and filtering DataFrame rows and columns
Delete DataFrame rows and columns
Modify DataFrame rows and columns
Week 2: Pandas Library Basics #2
Data preprocessing using the Pandas library
Review of selecting and filtering DataFrame rows and columns
Review of deleting DataFrame rows and columns
Review of DataFrame row and column modifications
Create a DataFrame group
Delete duplicate data
Find NaN and change it to another value
Using the apply function
Extract unique values from a column and check the number
Merging two DataFrames
Week 3: Data Visualization with Matplotlib and Seaborn Libraries
Understanding and Creating Bar Charts
Understanding and Creating Pie Charts
Understanding and Creating Line Charts
Understanding and Creating Scatter Charts
Understanding and Creating Heat Map Charts
Understanding and Creating Histogram Charts
Understanding and Creating Box Charts
Week 4: Linear Regression Theory and Practice
What is linear regression?
Training and cost function of linear regression models
Optimization methods for linear regression models
Batch gradient descent
Stochastic gradient descent
Mini-batch gradient descent
polynomial regression
Linear model with regulation
Ridge regression
Lasso regression
ElasticNet
Early Stopping
Week 5: Linear Classification Theory and Practice
What is logistic regression?
Training and cost function of a logistic regression model
What is a support vector machine?
Classification of support vector machines
Hard margin classification
Soft Margin Classification
Week 6: Decision Tree Model Theory and Practice
What is a decision tree model?
Decision Tree Learning and Visualization
Predict
Class probability estimation
CART training algorithm
Computational complexity
Genie impurity or entropy
regulatory parameters
return
Week 7: Ensemble Model Theory and Practice
What is an ensemble model?
Voting-based classifier
Bagging and pasting
Bagging and pasting in scikit-learn
oob rating
Random patches and random subspaces
Random Forest
Extra Tree
Feature Importance
Boosting
Adaboost
Gradient Boosting
Stacking
Week 8: Introduction to and Analysis of Kaggle Data Week 9: Kaggle Data Analysis
Please note before taking the class!
In this course, we will use Google Colab as the editor.
To ensure a balanced understanding of concepts and applications , the course is structured with a 50/50 ratio of theory to practice . Please check the curriculum for detailed information.
We provide lecture materials through our blog. You can find them at the following link. (Shortcut)
nice to meet you! Introducing rough coding.
Check out the VLOG of knowledge sharer Rough Coding now! 🐯
Recommended for these people
Who is this course right for?
People interested in machine learning
Machine Learning Beginner
Anyone who wants to learn Python visualization
People who want to learn data preprocessing and processing
I'm even happier that you're satisfied~!!
As you said, all the lecture contents and source code for the lecture are all on the blog,
so if you get stuck while studying, please refer to the blog. Thank you!
I will always cheer you on as you study hard.
Thank you.
-Rough Coding Dream-
I was so satisfied with the mentoring with Mr. Geoun Coding that I decided to take this course as well. As expected, his teaching skills are great, and the class content is very informative! Thank you~! Please upload more lectures!!
Ah! You also took a lecture after the mentoring!
I will come back at the end of October for a lecture on personalized recommendation systems :)
-Rough Coding Dream-
Instructor
This is a question about pension data
Practice data url: https://drive.google.com/drive/folders/149jcCyJFKKG5MFaPNWnYYqM2EkzgRz2P?usp=sharing
Create a new data folder (machine_learning_data) and upload files
If you go to the above location, you will see a shared folder called "machine_learning_data",
but there are only jpg files and cvs files in it, and I could not find any files related to the lecture.
If I am looking for the wrong location, please let me know.
It was great that I could apply the basic grammar of Python to modeling and even case studies right in Kaggle! I can't believe this level of quality is available for free lectures.. I'm looking forward to the series of lectures :)