Machine Learning That Even My Mom Can Do (Basic Practice)

Name: Machine Learning That Even My Mom Can Do (Basic Practice)
Price: 22270 KRW

The first step in learning AI: We've created the perfect curriculum for AI beginners! Second, match the theories and code you've learned! Now it's time for the real world! This is the stage to solidify the foundation and confidence in your knowledge by unraveling the theories you've learned into code.

25 learners are taking this course

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이론 실습 모두

하이퍼파라미터

EDA

Machine Learning(ML)

What you will learn!

⭐ Model Parameters VS Hyperparameters
⭐ Understanding How Hyperparameters Work for Each Model
⭐ Basic Knowledge for Handling Data
⭐ Practice with Classification, Regression, Clustering, and Recommendation Systems
⭐ Hyperparameter Tuning Techniques

Theory is the stepping stone for various code applications.
As a non-major, I won the grand prize and excellence award in contests, the first prize in competitions, and the grand prize and excellence award for projects in just 5 months .
You need to know the principles to be able to apply them to various situations and data.
When learning about artificial intelligence for the first time, various terms are thrown around, and the learning order felt unfamiliar while learning according to the curriculum at the institution . We thought a lot about it and modified and arranged the order so that even beginners can follow along with as little inconvenience as possible .
Rather than simply explaining mathematical and statistical concepts, when related references are made to models or indicators, the necessity of the corresponding formula or concept is mentioned, making it much easier to understand and convince, making learning smoother.

We use only the minimum mathematical and statistical concepts necessary for understanding, and even then, we have organized them all into examples for easy understanding.
By applying what you learned in the theory lectures directly to practice, you can check your knowledge and apply the content to code, completely destroying your fears and worries about entering the field of artificial intelligence.
Those who do not know about machine learning will be able to learn systematically and broadly without feeling burdened, and those who know about machine learning will be able to establish the concept accurately once again.
This is a basic course that covers almost all parts of machine learning that can be intuitively understood, and concepts such as SVM, ROC-AUC, and dimensionality reduction natural language processing (NLP) are covered in the advanced machine learning theory.
Since all lectures focus on future deep learning lectures, we recommend this course to those who want to build a solid foundation from machine learning .

Recommended for
these people

Who is this course right for?

⭐ Machine Learning Even My Mom Can Do (Basic Theory) Student
⭐ Those who need to understand hyperparameters
⭐ People who want to do machine learning-related practice

Need to know before starting?

Python Basics
NumPy, Pandas Basics

Hello
This is

136

Learners

Reviews

Answers

4.9

Rating

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인공지능 사관학교 5기 수료

시계열 농산물 가격 예측 프로젝트 대상

케글 경진대회 1등 (200 中)

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한국인공지능협회 주관 AI활용 사회문제 해결 공모전 최우수상

호남 ICT이노베이션 디지털 신기술 공모전 우수상

Curriculum

All

16 lectures ∙ (6hr 47min)

Course Materials:

Lecture resources

Section 1. Lecture materials

2 lectures

1. PPT + Practice Code
2. Practice Code (Answer)

Section 2. Parameter? Is it something you eat?

1 lectures ∙ (11min)

3. Model Parameters VS Hyperparameters
11:48

Section 3. Learn Classification Model Hyperparameters!

3 lectures ∙ (48min)

4. Decision Tree Hyperparameters
10:02
5. Voting, Bagging Hyperparameters
12:14
6. Boosting Hyperparameters
25:56

Section 4. Theory loaded, bring on the practice (Titanic survivor prediction practice)

2 lectures ∙ (1hr 24min)

Section 5. Let's learn about linear regression model hyperparameters!

1 lectures ∙ (12min)

Section 6. Theory loaded, let's practice! (Diamond Price Prediction Practice)

2 lectures ∙ (2hr 3min)

Section 7. Let's explore clustering model hyperparameters!

1 lectures ∙ (17min)

Section 8. Theory ready, let's get to practice (RFM-based customer consumption pattern clustering)

1 lectures ∙ (36min)

Section 9. Theory Ready, Practice Begins (Netflix Movie Recommendations, Movielens Rating Prediction)

1 lectures ∙ (36min)

Section 10. Hyperparameter Optimization and Final Practice (Water Quality Prediction Practice)

2 lectures ∙ (37min)

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Machine Learning That Even My Mom Can Do (Basic Practice)

What you will learn!

📢 This lecture is for non-majors.

Artificial intelligence, solved very easily!

I have excluded statistical and mathematical concepts as much as possible!

Theory lectures, don't be afraid!

Machine Learning That Even Our Mom Can Do (Basic Practice)

Artificial Intelligence (AI)