Course Description
This course will cover the basics of product recommendation systems and how they work. Students will learn about different types of recommendation algorithms and how to implement them in Python. They will also learn about data preprocessing, feature engineering, and model evaluation techniques. The course will focus on hands-on projects to apply these concepts to real-world datasets and build practical product recommendation systems.
Learning Objectives
Upon completing this course, students will be able to:
Understand the different types of recommendation algorithms and their strengths and weaknesses
Preprocess data and perform feature engineering for recommendation systems
Evaluate and compare different recommendation models
Implement a variety of recommendation algorithms in Python
Build practical product recommendation systems using real-world datasets
Prerequisites:
Basic knowledge of Python programming language
Familiarity with data structures and algorithms
Basic understanding of machine learning concepts
Course Outline:
Introduction to Product Recommendation Systems
Overview of recommendation systems
Types of recommendation algorithms
Data preprocessing and feature engineering for recommendation systems
Collaborative Filtering
User-based collaborative filtering
Item-based collaborative filtering
Matrix factorization
Content-Based Recommendation
Content-based filtering
Vectorization techniques
Similarity measures
Hybrid Recommendation Systems
Combining collaborative filtering and content-based filtering
Building hybrid recommendation models
Deep Learning-based Recommendation Systems
Introduction to neural networks
Building deep learning-based recommendation models
]Evaluation and Metrics
Evaluation metrics for recommendation systems
A/B testing for recommendation systems
Project Work
Students will work on a project to build a product recommendation system using a real-world dataset. They will apply the concepts learned in the course and implement various recommendation algorithms to evaluate and compare their performance.
Throughout the syllabus, students will use popular data science and machine learning libraries such as NumPy, Pandas, and Scikit-Learn to build and evaluate their product recommendation systems. They will also learn how to perform exploratory data analysis, preprocess and clean data, perform feature engineering, and use collaborative filtering and content-based filtering to extract valuable information from the data.
The course will cover a range of recommendation algorithms, including user-based collaborative filtering, item-based collaborative filtering, matrix factorization, content-based filtering, and hybrid recommendation systems. Students will also learn how to build deep learning-based recommendation models using neural networks.
Students will learn how to evaluate the performance of their models using various performance metrics. They will also learn about A/B testing and how to conduct experiments to evaluate the effectiveness of their recommendation systems.
How can Codersarts help in this project?
Consultation: Codersarts can provide expert consultation on your project and offer guidance on best practices for preprocessing text data, model selection, and deployment.
Custom Development: Codersarts can develop custom software solutions for your project, including data preprocessing tools, feature extraction scripts, and machine learning models for toxic comment classification.
Code Review: Codersarts can review your code and offer suggestions for improving efficiency, scalability, and maintainability.
Training: Codersarts can provide online training courses on natural language processing and machine learning to help you and your team develop the skills you need for your project.
Contact us
If you need help with the above project contact us today, you can visit our website at www.codersarts.com or www.training.codersarts.com/and use the contact form on the "Contact Us" page to send us a message. You can also send us an email at contact@codersarts.com or directly chat with us through our 24/7 online chat support.
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