Diana Certified AI Recommender Systems Expert

Categories: Diana AI
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About Course

Schedule: MON – FRI 4 hours (Time: 9 AM to 1 PM & 2 PM to 6 PM ) |SAT/SUN 8 hours (9 AM to 6 PM)

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.

Course Modules

An Introduction to Basic Concepts on Recommender Systems

  • Concepts on Recommender Systems

A Machine Learning Approach to Recommend Suitable Crops and Fertilizers

Content-Based Recommender Systems

Product or Item-Based Recommender System

Temporal Change Analysis-Based Recommender System

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