What Will You Learn?

• Introduction to Recommender Systems: Understanding the fundamentals, importance, and types of recommender systems.
• Data Collection and Preprocessing: Gathering and cleaning data, feature engineering for recommender systems.
• Collaborative Filtering Techniques: Implementing user-based, item-based, and matrix factorization methods.
• Content-Based Filtering Techniques: Building systems based on item and user features, similarity measures.
• Hybrid Recommender Systems: Combining collaborative and content-based methods for robust recommendations.
• Advanced Techniques: Applying deep learning, sequential recommendations, and context-aware systems.
• Evaluation and Metrics: Measuring performance using precision, recall, RMSE, and conducting offline and online evaluations.
• Scalability and Real-Time Recommendations: Handling large-scale data and implementing real-time recommendation systems.
• Ethical Considerations and Bias: Addressing bias, privacy, fairness, and transparency in recommender systems.
• Hands-On Projects and Case Studies: Engaging in project-based learning and analyzing successful real-world systems.
• Tools and Technologies: Utilizing Python, R, Scikit-learn, TensorFlow, PyTorch, Spark MLlib, and cloud platforms.
• Certification and Career Guidance: Preparing for certification, exploring career opportunities, and job role insights in AI and recommender systems.