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강의

Python으로 추천 엔진 만들기

중급기술 수준

업데이트됨 2024. 4.

Machine Learning 기법을 활용해 Python으로 추천 엔진을 구축하는 방법을 학습합니다.

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PythonMachine Learning

4시간

16 동영상

60 연습 문제

4,850 XP

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성취 증명서

수천 개 기업의 학습자들이 사랑하는

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강의 설명

이제 우리는 온라인에서 개인화된 경험을 기대합니다. Netflix가 시청할 프로그램을 추천하거나 온라인 소매점이 함께 구매할 만한 상품을 제안하는 것처럼요. 그런데 이런 추천은 어떻게 만들어질까요? 이 강의에서는 직접 추천 엔진을 만들기 위해 필요한 모든 내용을 배웁니다. 실습을 통해 가장 흔히 쓰이는 두 가지 방식인 협업 필터링과 콘텐츠 기반 필터링을 익힙니다. 이어서 Jaccard 거리와 코사인 유사도 같은 유사도 측정 방법과, 루트 평균 제곱 오차(RMSE)를 사용해 테스트 데이터에서 추천 품질을 평가하는 방법을 배웁니다. 강의가 끝나면 직접 영화 추천 엔진을 만들어 보게 되며, Python 실력을 바탕으로 어떤 산업에도 적용할 수 있는 추천 시스템을 구현할 수 있게 됩니다.

선수 조건

Supervised Learning with scikit-learn

1

Introduction to Recommendation Engines

What problems are recommendation engines designed to solve and what data are best suited for them? Discern what insightful recommendations can be made even with limited data, and learn how to create your own recommendations.

What are recommendation engines?

Recommendation engines vs. predictions

Identifying the correct data for recommendation engines

Implicit vs. explicit data

Non-personalized recommendations

Introduction to non-personalized recommendations

Improved non-personalized recommendations

Combining popularity and reviews

Non-personalized suggestions

Finding all pairs of movies

Counting up the pairs

Making your first movie recommendations

2

Content-Based Recommendations

Discover how item attributes can be used to make recommendations. Create valuable comparisons between items with both categorical and text data. Generate profiles to recommend new items for users based on their past preferences.

Intro to content-based recommendations

Why use content-based models?

Creating content-based data

Understanding the content-based data

Making content-based recommendations

Comparing individual movies with Jaccard similarity

Comparing all your movies at once

Making recommendations based on movie genres

Text-based similarities

Instantiate the TF-IDF model

Creating the TF-IDF DataFrame

Comparing all your movies with TF-IDF

Making recommendations with TF-IDF

User profile recommendations

Build the user profiles

User profile based recommendations

3

Collaborative Filtering

Discover new items to recommend to users by finding others with similar tastes. Learn to make user-based and item-based recommendations—and in what context they should be used. Use k-nearest neighbors models to leverage the wisdom of the crowd and predict how someone might rate an item they haven’t yet encountered.

Collaborative filtering

Pivoting your data

Finding similar users

Challenges with missing values

Compensating for incomplete data

Finding similarities

User-based to item-based

Similar and different movie ratings

Finding similarly liked movies

Using K-nearest neighbors

Stepping through K-nearest neighbors

Getting KNN data in shape

KNN predictions

Item-based or user-based

Comparing item-based and user-based models

Which should you choose?

4

Matrix Factorization and Validating Your Predictions

Understand how the sparsity of real-world datasets can impact your recommendations. Leverage the power of matrix factorization to deal with this sparsity. Explore the value of latent features and use them to better understand your data. Finally, put the models you’ve discovered to the test by learning how to validate each of the approaches you’ve learned.

Dealing with sparsity

Matrix sparsity

Limited data in your rows

Matrix multiplication

Matrix factorization

Identifying latent features

Information loss in factorization

Singular value decomposition (SVD)

Normalize your data

Decomposing your matrix

Recalculating the matrix

Making recommendations with SVD

Validating your predictions

Calculating RMSE

Comparing recommendation methods

Python으로 추천 엔진 만들기

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LinkedIn 프로필, 이력서 또는 CV에 이 인증서를 추가하세요
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19백만 명 이상의 학습자와 함께 Python으로 추천 엔진 만들기을(를) 시작하세요!

DataCamp for Mobile을 통해 데이터 분석 능력을 향상시키세요.

모바일 강좌와 매일 5분 코딩 챌린지를 통해 이동 중에도 학습 효과를 높이세요.