Netflix Movie Recommendation System using Machine Learning
Working of Netflix Movie Recommendation System
Srikanth Varma,
Lead DSML Instructor at Scaler
About this Free Netflix Movie Recommendation System using Machine Learning Course
Embark on a journey into the world of recommendation systems with our Netflix Movie Recommendation System course. Explore the intricacies of collaborative-based approaches to provide personalized movie recommendations for Netflix users. From problem definition to model evaluation, delve into data preprocessing, exploratory analysis, and similarity matrix computation. Learn to implement state-of-the-art techniques, including Surprise library integration, matrix factorization, and SVD++, to develop and fine-tune recommendation models.
5
Audio: English
Subtitles: English
Duration
3h 6m (1 Modules)Course Level
BeginnerCertificate
IncludedWhat you’ll learn
The skills that you would learn after taking up this Netflix Movie Recommendation System using Machine Learning online course are:
- Mapping business problems to machine learning (ML) problems.
- Formulating ML problems based on business objectives.
- Exploratory data analysis (EDA) techniques for preprocessing and analysis.
- Handling temporal train-test splits for time-series data.
- Computing similarity matrices for user-user and movie-movie relationships.
- Using the Surprise library for building recommendation models.
- Feature engineering and featurization techniques for regression tasks.
- Transforming data for compatibility with specific ML libraries (e.g., Surprise).
- Implementing ML models such as XGBoost for regression tasks.
Course Content
1 Modules | 28 Lessons | 3h 6m
Certificate for Free Netflix Movie Recommendation System using Machine Learning
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Instructor of this course
Lead DSML Instructor at Scaler
2000+ Students on Scaler Platform
600+ Hours of Lectures Delivered
5 Star Instructor on Scaler
9 Courses
- Co-Founder & Principal Instructor, Applied AI & AppliedRoots
- Senior ML Scientist @ Amazon, Palo Alto and Bangalore
- Co-Founder, Matherix Labs
- Research Engineer, Yahoo! Labs
- Masters from IISc Bangalore, Gate 2007(AIR 2)
- 13 years of experience in AI and Machine Learning
Key Features of this Netflix Movie Recommendation System Course
- Gain insights into recommendation systems, specifically focusing on movie recommendations for Netflix users.
- Engage in practical exercises and projects to reinforce learning and implementation of recommendation algorithms.
- Learn techniques for cleaning and preparing data for recommendation model development.
- Explore data distribution, temporal trends, and address challenges such as the cold start problem.
- Understand and implement user-user and item-item similarity matrices for recommendation.
- Evaluate and compare the performance of different recommendation models using appropriate metrics.
- Gain insights into the practical application of recommendation systems in the context of the Netflix movie recommendation engine.
Pre-requisites for Netflix Movie Recommendation System Course
Prior to embarking on this course, familiarity with the following concepts is recommended:
- Familiarity with programming concepts, preferably in Python, for implementing recommendation algorithms.
- Basic knowledge of machine learning concepts, including supervised and unsupervised learning.
- Proficiency in data analysis techniques using libraries like pandas and numpy.
- Understanding of linear algebra and statistical concepts, beneficial for understanding recommendation algorithms.
- Basic knowledge of recommendation system concepts and techniques is recommended but not mandatory.
- A keen interest in understanding and developing recommendation systems specifically for movie recommendations.
Who should learn this Netflix Movie Recommendation System Course?
This course is ideal for:
- Data scientists and machine learning enthusiasts interested in recommendation systems.
- Software developers aiming to specialize in building recommendation engines.
- E-commerce professionals seeking to enhance their understanding of recommendation algorithms.
- Students pursuing degrees or certifications in data science or machine learning.
- Anyone intrigued by the inner workings of recommendation systems and their application in the entertainment industry.