Recommendation Engine - Basics

Instructor: EDUCBA

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1 module

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Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

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There is 1 module in this course

This hands-on course guides learners through the complete lifecycle of building a movie recommendation system using Python. Beginning with a conceptual overview of recommendation engines and collaborative filtering techniques, learners will identify real-world applications and articulate how these systems drive personalization across platforms. The course progresses through environment setup using Anaconda and dataset preparation, ensuring participants can organize, configure, and manipulate data efficiently.

Using the Surprise library, learners will construct machine learning models, validate performance using cross-validation techniques (including RMSE and MAE), and interpret prediction accuracy. Learners will write Python functions to generate personalized movie predictions, gaining practical experience in model evaluation, prediction logic, and iterable handling using tools like islice. By the end of the course, learners will be able to analyze datasets, implement algorithms, and deploy predictive features in a streamlined and reproducible manner. Through interactive coding and progressive exercises, learners will apply, analyze, and create recommendation solutions applicable in real-world data science workflows.

This module introduces learners to the foundational concepts and technical workflow of building a recommendation engine using Python. It begins with a conceptual overview of recommendation systems and collaborative filtering, then transitions into preparing the development environment and datasets using Anaconda and the Surprise library. Finally, learners will construct, evaluate, and deploy a predictive model capable of generating personalized movie recommendations using real user data. The focus is on practical application, model evaluation with cross-validation, and generating top predictions through structured Python functions.

What's included

8 videos4 assignments

8 videosTotal 43 minutes

Introduction to Project5 minutesPreview module
Collaborative Filtering4 minutes
Anaconda Setup Dataset Download4 minutes
Surprise Data frame7 minutes
Cross Validation Model3 minutes
Train Test Prediction3 minutes
Function For Prediction8 minutes
Movie Prediction4 minutes

4 assignmentsTotal 60 minutes

GRADED - Building a Recommendation Engine from Scratch30 minutes
Introduction and Collaborative Filtering Basics10 minutes
Dataset Preparation and Surprise Library10 minutes
Model Building and Movie Prediction10 minutes

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Instructor

EDUCBA

99 Courses90,548 learners

Offered by

EDUCBA

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