10 Sections
34 Lessons
10 Weeks
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Discover the Career
EA2C15L1 What does a Recommendation Systems Engineer do & why it matters today
2
1.1
EA2C15L2 Evolution of recommendation systems — from Amazon to Netflix
1.2
EA2C15L3 Industry overview, myths vs. realities, and future relevance
Is This Career Right for You?
EA2C15L4 Personality fit – who thrives in this profession
2
2.1
EA2C15L5 Interests & aptitudes – analytical vs. creative balance
2.2
EA2C15L6 Cognitive and problem-solving traits of data engineers
Your School & Subject Choices
EA2C15L7 What to choose after 10th – subjects, boards, foundations
6
3.1
EA2C15L8 What to Choose After 12th — Streams, entrance focus, and coaching
3.2
EA2C15L9 Building early foundations — math, coding, and statistics
3.3
EA2C15L10 Extracurriculars, projects, and competitions that build your profile
3.4
EA2C15L12 Scholarships, Olympiads & alternate academic routes
3.5
EA2C15L11 Top Colleges & Universities — India + Global (fee, cutoff, ranking)
3.6
EA2C15L13 Scholarships, Olympiads & alternate academic routes
Academic Journey & College Life
EA2C15L14 Course Duration, Levels, and Structure (UG, PG, Specializations)
3
4.1
EA2C15L15 Year-by-Year Subject Overview — with examples and electives
4.2
EA2C15L16 Teaching Style — Labs, projects, and research-based learning
4.3
EA2C15L17 Assessment & Exams — Types of tests and grading patterns
Core Technical Foundations
EA2C15L18 Introduction to Machine Learning and Data Science
3
5.1
EA2C15L19 Mathematics for Recommendation Systems — Linear Algebra, Probability, and Statistics
5.2
EA2C15L20 Programming Fundamentals — Python, R, and SQL
5.3
EA2C15L21 Data Processing and Pipelines — Pandas, NumPy, and Spark
Recommendation Algorithms
EA2C15L22 Collaborative Filtering — User-based and Item-based methods
5
6.1
EA2C15L23 Content-based Filtering — Similarity Metrics and Feature Engineering
6.2
EA2C15L24 Hybrid Models — Blending collaborative and content-based systems
6.3
EA2C15L25 Context-aware & Knowledge-based Recommenders
6.4
EA2C15L26 Deep Learning Approaches — Neural Collaborative Filtering, Autoencoders
6.5
EA2C15L27 Evaluation Metrics — Precision, Recall, MAP, NDCG, and A/B Testing
Real-World Tools & Skills
EA2C15L28 Frameworks & Libraries — TensorFlow, PyTorch, Scikit-learn
3
7.1
EA2C15L29 Data Handling — Big Data, Cloud, and APIs
7.2
EA2C15L30 MLOps for Recommendation Systems — Deployment & Monitoring
7.3
EA2C15L31 Communication, teamwork, and documentation best practices
Real-World Experience & Internships
EA2C15L32 Internships — When, where, and how to get them
2
8.1
EA2C15L33 Building Projects — Movie, E-commerce, and Music Recommenders
8.2
EA2C15L34 Participating in Kaggle and open-source projects
Career Growth, Roles & Income
EA2C15L35 First Job Roles — Data Engineer, ML Engineer, Recommender System Specialist
4
9.1
EA2C15L36 Mid-Level Growth — Senior ML Engineer, Applied Scientist
9.2
EA2C15L37 Leadership & Entrepreneurship — Building AI-driven startups
9.3
EA2C15L38 Salary Insights — Entry / mid / senior levels in India & abroad
9.4
EA2C15L39 Career Maps — 5-year, 10-year, and 20-year trajectories
Future Trends & Personal Roadmap
EA2C15L40 Emerging Trends — Generative AI and personalization at scale
4
10.1
EA2C15L41 Ethical & Responsible Recommendation Systems — Bias, fairness, and transparency
10.2
EA2C15L42 Related Careers — Data Scientist, NLP Engineer, Product Manager
10.3
EA2C15L43 5-year Action Plan — From student to expert
10.4
EA2C15L44 Final Career Checklist and Resources
Recommendation Systems Engineer
Curriculum
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