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[Renewed] Python Machine Learning Bootcamp for Beginners (Easy! Learn by Solving Real Kaggle Problems) [Data Analysis/Science Part2]

Name: [Renewed] Python Machine Learning Bootcamp for Beginners (Easy! Learn by Solving Real Kaggle Problems) [Data Analysis/Science Part2]
Price: 57750 KRW
Rating: 4.9 (113 reviews)

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

(4.9) 수강평 113개

강의소개.상단개요.수강생.short

난이도 초급

수강기한 무제한

funcoding

Machine Learning(ML)

Kaggle

Pandas

Python

Machine Learning(ML)

Kaggle

Pandas

Python

먼저 경험한 수강생들의 후기

4.9

5.0

mizzbaby

30% 수강 후 작성

If it's a lecture by 잔재미코딩, it's a must. I took the complete SQL course and complete Python course, successfully transitioned my career to data analyst, and now I'm working overseas. (I purchased all the courses and posted questions with my previous account, but I lost that account and had to create a new one😭😭) Even someone like me with absolutely no coding experience could understand and complete my own projects, so you can imagine how well he explains things. I've taken courses from various instructors on Inflearn, but personally, I don't think anyone can match his teaching skills. He doesn't just read text monotonously but explains why things work in a way that really sticks in your ears. He's simply the best. Now I have a new goal, so I'll continue the journey from machine learning to deep learning together. Thank you always for the great lectures.

5.0

YuJin Lee

94% 수강 후 작성

The process of planning and filming the lectures must have been very difficult; it was truly a packed lecture. It was worth every penny I spent on the purchase. If your goal is to get an introduction to machine learning with no prior knowledge, this lecture is perfect for grasping the overall framework. However, just listening to the lecture may not be enough. You need to review the class materials, lecture content, and additional searches, including GPT. If you want to make it your own, I think these additional efforts are essential. The lecture materials are also very detailed, making it easy to learn just by looking at the code without the lecture. I think it's even better because the lectures have been recently updated to be up-to-date. Thank you for providing such a high-quality lecture :)

5.0

hhs834373

92% 수강 후 작성

As you said, it was a lecture that was very helpful in grasping the big picture. I found additional detailed theories and mathematical knowledge in the knowledge I learned in school and studied it, which was also a new kind of fun. Above all, it was a lecture that helped me quickly learn various models through practice and start to get a feel for them.

강의상세_배울수있는것_타이틀

Introduction to Machine Learning
sklearn and Python Machine Learning
Introduction to Kaggle
Machine Learning Classification Methods
Machine Learning Regression Techniques
Machine Learning Clustering Techniques
One-hot encoding, hyperparameter tuning, and other practical techniques

The official course selected for in-house training by top tech companies!
For beginners starting Python machine learning
A highly polished course

This course is based on the data analysis/science roadmap and designed for beginners learning Python machine learning for the first time
Drawing from the instructor's own early struggles when first learning machine learning,
the course is structured to help you understand essential concepts and key practical techniques by solving various real-world problems
Through this approach, you can apply machine learning to real problems in a short period without failing

This course is currently being used as the official in-house Python machine learning training course at one of the major tech companies (NAVER, Kakao, LINE, Coupang, Baemin)

This course has been newly renewed to reflect previous feedback

I'm a data beginner! Where should I start with complex machine learning/AI technology?

Machine learning/AI technology has complex concepts and various techniques for applying them to real-world problems, making the content very extensive.
When learning for the first time, you should start with machine learning basics, combining essential concepts with techniques for applying them to real-world problems in an appropriate balance.
Once you get a feel for machine learning technology based on this, you can then learn AI technology building on that foundation
The more numerous the theories and complex the technologies, the more you need to build up step by step, focusing on the essential parts, in order to actually apply them

This course has been improved based on insights gained from the instructor's numerous failures!

Rather than focusing too much on deep principles like mathematics/statistics or listing all outdated technologies that won't even be used
It is structured to help you learn essential concepts and key techniques for applying them to real-world problems by solving practical problems
There are various techniques to apply to real-world problems. To help you master these, you will learn different machine learning techniques through practical problems
Using the most well-known problem with abundant resources, we apply as many practical techniques as possible and learn various methods that can be considered when actually utilizing machine learning in real-world applications
We'll go through the entire machine learning process by downloading data from Kaggle, the most famous site for data prediction problems, making predictions, and even submitting the final results.

강사도 몇 차례 실패 끝에, 이와 같은 순서로 학습해서, 결국 현업에서도 잘 활용하고 있습니다.

I want to use machine learning technology, even if just lightly - how can I do that?

This is something the instructor found frustrating a long time ago. First, based on real problems, learn how to apply machine learning techniques.
Even if you understand basic machine learning concepts, the reason it's difficult to apply them to real problems is because there are various techniques used when applying them to actual problems.
By following along at the code level with various techniques that can be applied to real problems, and hearing explanations right away whenever you need to understand related concepts, you can utilize the entire process, even if just lightly.

Once you become familiar with related technologies through this, you can understand overall machine learning technology in a short time, and even apply it immediately

I'm new to machine learning! What skills do I need to learn first before taking this course?

You can take this course if you can use PythonIt's possible if you can use pandas and visualization techniques
For those who are not familiar with related technologies, we provide a data analysis/science roadmap that allows you to learn systematically, taking difficulty levels into consideration
In particular, if you take it together with the Python Data Analysis for Beginners course explained at the bottom of this page in the data analysis/science roadmap, you can learn data handling techniques with Python sequentially

I'm a beginner considering a career in data, how can I learn systematically?

The data field has various theories and technologies, so if you approach it incorrectly, it can be difficult to learn even after a long time. I have also failed several times. However, if you focus on learning the core technologies, it can be easier than you think.

Divide the core data-related skills into data collection, storage, analysis, and prediction tasks, and learn the related technologies sequentially. If you build knowledge about each business domain (called domain knowledge) on top of this, you can gain competitiveness. In this regard, I've created a data analysis/science roadmap so you can sequentially learn core data-related skills while gradually increasing the difficulty level. You can also check the related roadmap at the bottom of this page.

We've created a video that explains in detail data-related careers and the entire data analysis/science process. By referring to this video, depending on what you want to do, you can easily learn the data process in a short time on your own without trial and error!

These are verified courses that many people have studied over the years and given excellent feedback on.

Verified by 20,000 paid students online and offline over 10 years!
Don't waste your time!
Different instructors can make a difference in IT courses!
If you're meticulous and rational, you can do it!

How difficult is it to learn machine learning technology?

If you can use Python, it's not difficult!
When first learning machine learning, the most challenging part is studying the math, statistics, and probability needed to understand the related theories. Even if an instructor who has spent decades mastering these skills explains them easily, it takes learners a very long time to grasp them.

Rather than deeply studying related theories and profound mathematical principles from the start, lightly understand the concepts and learn how to write machine learning code with real problems. Instead of aiming for the Top 1% from the beginning, first aim for the Top 20% in data prediction and learn coding methods and techniques that can be applied to practical problems. By understanding concepts to the extent you can grasp them and actually applying machine learning code, you'll become familiar with it, and you'll be able to understand and utilize machine learning technology that seemed vague when only learning theory.

Can I participate in Kaggle competitions that solve real data problems, which seem to be quite common these days?

This course is also structured to allow you to learn step-by-step by applying techniques one by one based on actual Kaggle problems and data.

There's a big difference between learning how to use each machine learning technique and the code and steps needed to solve real-world problems.
We proceed step by step through analyzing, processing, and predicting actual data.
And I explain the techniques needed to understand each step. We even go as far as submitting the prediction results.
So, it's designed to help you understand practical application methods without getting exhausted from learning only theory for a long time.

Since this course is designed for beginners, it aims for the top 20% by focusing on the essential techniques you must cover!

Designed to help you understand and apply machine learning techniques in practice.

This is a primer course for those learning machine learning for the first time
With real-world experience, well-organized materials and examples, the instructor teaches as if learning it for the first time!
So that even beginners can apply machine learning techniques up to the Top 20% in a short time!

Focusing on key machine learning technologies that are still being used today!
Based on actual kaggle problems and data
→ What machine learning techniques are available
→ What steps are involved in analyzing, processing, and predicting with real data
→ Including practical techniques needed for real-world application such as Feature Engineering, Hyper Parameter Tuning, Voting, Encoding, etc.

Applying it to real problems is fun, and it's truly exciting when the predictions turn out well! I hope to share the little joys of machine learning with reasonable, good people.

Enhance your learning with easy-to-understand summarized materials and code, backed by lectures!

There's an overflow of materials and information.
After listening to lectures that explain in detail using summary materials made to help you understand exactly what's necessary,
from then on, whenever you think 'Oh, there was something about this?', you can immediately understand just by looking at the materials.

Materials that are concisely written with only the essential parts
to help you understand and utilize the relevant topics
And actual code files for applying machine learning to real problems

Test code is provided in a format that allows code testing (Jupyter Notebook format), and basic theory is provided as PDF files.
Machine learning-related PDF materials are provided so you can access them anytime like an ebook. (However, copying and downloading of related materials are restricted due to copyright issues)

This is an IT lecture series created with
careful thought so you can feel 'Ah! This is really different!'
We ask that only those who are reasonable,
considerate of others, and
looking to build good relationships enroll!

Learn Systematically
Janjae Mi Coding Dave Lee's Roadmap 🔑

Developer, Data Analyst, and Data Scientist Career Roadmap!

From web/app development to data analysis and AI, we provide an A to Z roadmap to build a solid foundation in a short time. IT technologies are closely interconnected, and integrating them enables web/app services and data science. By gradually increasing difficulty and mastering core technologies, you can learn efficiently, understand systems and data comprehensively, and grow into a competitive developer or data professional. To achieve this, we have prepared a roadmap that systematically organizes the core technologies of each field.

1. The Fastest Complete Data Roadmap

We've created a video that explains this roadmap, data-related careers, and the entire data analysis/science process in detail. By referring to this video, you can easily learn the data process without trial and error in a short time, even on your own!