Masterclass details

Recommendation systems power some of the world's most successful digital products. Whether it's Netflix suggesting your next favorite show, Amazon recommending products, or Spotify curating playlists, recommendation engines are at the heart of personalized user experiences. Learning how to build these systems is a valuable skill for aspiring Data Scientists, Machine Learning Engineers, and AI professionals.

In this live masterclass, Sumit Shukla, Director & AI Head at Scaletrix.ai, will guide you through building a Netflix-style Recommendation System using Python and Machine Learning. With over a decade of experience in AI, Data Science, and analytics, Sumit will explain not just the technical concepts but also the business impact behind recommendation engines.

The session starts with a reality check: many learners know ML algorithms but struggle to apply them to real-world business problems. Through this practical use case, you'll learn how companies leverage user behavior, preferences, and engagement data to deliver highly personalized recommendations that improve customer retention and revenue.

You'll gain hands-on exposure to recommendation system architectures, machine learning workflows, and evaluation techniques while understanding how such projects can strengthen your portfolio for top Data Science and AI roles.

🔍 What You’ll Learn

• Fundamentals of recommendation systems
• Building a Netflix-style recommendation engine using Python
• Collaborative Filtering and Content-Based Filtering techniques
• Data preprocessing and feature engineering
• Evaluating recommendation model performance
• Real-world business applications of recommendation systems
• How to showcase ML projects in resumes and interviews

🚀 Session Outcome

By the end of this masterclass, you'll understand how recommendation systems work, gain practical experience building one using Python and ML, and create a portfolio-ready project that demonstrates real-world AI and Data Science skills.

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